hydra-core

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.

This is intended for trusted application configuration. However, if untrusted input controls _target_, the config becomes a callable-selection mechanism. A malicious config can select a callable capable of executing code or commands and provide attacker-controlled arguments.

This issue is the same general class of problem discussed by Unit 42 for downstream AI/ML libraries such as NVIDIA NeMo, where untrusted model metadata was passed into Hydra instantiate:

https://unit42.paloaltonetworks.com/rce-vulnerabilities-in-ai-python-libraries/

Hydra 1.3.4 includes a blacklist for some dangerous _target_ values. That blacklist is defense-in-depth and is not a complete security boundary. The blacklist is not present in the released hydra-core 1.3.3 package, so this issue should not be described as a bypass of a released 1.3.3 blacklist.

Impact

A successful attack can execute code in the process that calls hydra.utils.instantiate(). The impact is limited to the privileges and environment of that process.

Potential impact includes:

• Reading files, credentials, environment variables, or data accessible to the process
• Modifying files, outputs, checkpoints, or application state writable by the process
• Terminating or disrupting the process

Affected Usage

Applications and libraries are affected when they pass untrusted or semi-trusted config, model metadata, CLI overrides, or other externally controlled data to hydra.utils.instantiate() without constraining which targets may be instantiated.

Trusted application-owned configuration is not affected in the same way.

Remediation

Hydra 1.3.4 hardens the existing behavior by adding a blacklist of obvious dangerous targets. It is a substantial security improvement, and users remaining on the 1.3 release line should upgrade to 1.3.4 or a newer version.

The unreleased Hydra 1.4 development line uses an allowlist-based instantiation model that fully addresses this vulnerability class. The allowlist must come from trusted application code or another trusted channel, not from the untrusted config being instantiated.

Applications that consume untrusted or semi-trusted config should not pass it directly to hydra.utils.instantiate(). They should validate _target_ values against a trusted allowlist before instantiation.

Summary

hydra.utils.instantiate() resolves and calls Python objects from config. If an application passes untrusted config to instantiate(), an attacker who controls _target_ and its arguments can cause arbitrary code execution in the consuming process.

Hydra is not a network service. Exploitation requires a consuming application, library, or user workflow to load attacker-controlled config, CLI overrides, or model metadata and pass it to hydra.utils.instantiate().

Details

Hydra's instantiate API is designed to construct objects and call functions from configuration. For example:

component:
  _target_: package.module.Class
  arg: value

When this config is passed to hydra.utils.instantiate(), Hydra resolves _target_ and calls it with the provided arguments.