text-generation

A vulnerability in huggingface/text-generation-inference version 3.3.6 allows unauthenticated remote attackers to exploit unbounded external image fetching during input validation in VLM mode. The issue arises when the router scans inputs for Markdown image links and performs a blocking HTTP GET request, reading the entire response body into memory and cloning it before decoding. This behavior can lead to resource exhaustion, including network bandwidth saturation, memory inflation, and CPU overutilization. The vulnerability is triggered even if the request is later rejected for exceeding token limits. The default deployment configuration, which lacks memory usage limits and authentication, exacerbates the impact, potentially crashing the host machine. The issue is resolved in version 3.3.7.

A code injection vulnerability exists in the huggingface/text-generation-inference repository, specifically within the autodocs.yml workflow file. The vulnerability arises from the insecure handling of the github.head_ref user input, which is used to dynamically construct a command for installing a software package. An attacker can exploit this by forking the repository, creating a branch with a malicious payload as the name, and then opening a pull request to the base repository. Successful exploitation could lead to arbitrary code execution within the context of the GitHub Actions runner. This issue affects versions up to and including v2.0.0 and was fixed in version 2.0.0.

A vulnerability in huggingface/text-generation-inference version 3.3.6 allows unauthenticated remote attackers to exploit unbounded external image fetching during input validation in VLM mode. The issue arises when the router scans inputs for Markdown image links and performs a blocking HTTP GET request, reading the entire response body into memory and cloning it before decoding. This behavior can lead to resource exhaustion, including network bandwidth saturation, memory inflation, and CPU overutilization. The vulnerability is triggered even if the request is later rejected for exceeding token limits. The default deployment configuration, which lacks memory usage limits and authentication, exacerbates the impact, potentially crashing the host machine. The issue is resolved in version 3.3.7.

A code injection vulnerability exists in the huggingface/text-generation-inference repository, specifically within the autodocs.yml workflow file. The vulnerability arises from the insecure handling of the github.head_ref user input, which is used to dynamically construct a command for installing a software package. An attacker can exploit this by forking the repository, creating a branch with a malicious payload as the name, and then opening a pull request to the base repository. Successful exploitation could lead to arbitrary code execution within the context of the GitHub Actions runner. This issue affects versions up to and including v2.0.0 and was fixed in version 2.0.0.

A vulnerability in huggingface/text-generation-inference version 3.3.6 allows unauthenticated remote attackers to exploit unbounded external image fetching during input validation in VLM mode. The issue arises when the router scans inputs for Markdown image links and performs a blocking HTTP GET request, reading the entire response body into memory and cloning it before decoding. This behavior can lead to resource exhaustion, including network bandwidth saturation, memory inflation, and CPU overutilization. The vulnerability is triggered even if the request is later rejected for exceeding token limits. The default deployment configuration, which lacks memory usage limits and authentication, exacerbates the impact, potentially crashing the host machine. The issue is resolved in version 3.3.7.

A code injection vulnerability exists in the huggingface/text-generation-inference repository, specifically within the autodocs.yml workflow file. The vulnerability arises from the insecure handling of the github.head_ref user input, which is used to dynamically construct a command for installing a software package. An attacker can exploit this by forking the repository, creating a branch with a malicious payload as the name, and then opening a pull request to the base repository. Successful exploitation could lead to arbitrary code execution within the context of the GitHub Actions runner. This issue affects versions up to and including v2.0.0 and was fixed in version 2.0.0.

A vulnerability in huggingface/text-generation-inference version 3.3.6 allows unauthenticated remote attackers to exploit unbounded external image fetching during input validation in VLM mode. The issue arises when the router scans inputs for Markdown image links and performs a blocking HTTP GET request, reading the entire response body into memory and cloning it before decoding. This behavior can lead to resource exhaustion, including network bandwidth saturation, memory inflation, and CPU overutilization. The vulnerability is triggered even if the request is later rejected for exceeding token limits. The default deployment configuration, which lacks memory usage limits and authentication, exacerbates the impact, potentially crashing the host machine. The issue is resolved in version 3.3.7.

A code injection vulnerability exists in the huggingface/text-generation-inference repository, specifically within the autodocs.yml workflow file. The vulnerability arises from the insecure handling of the github.head_ref user input, which is used to dynamically construct a command for installing a software package. An attacker can exploit this by forking the repository, creating a branch with a malicious payload as the name, and then opening a pull request to the base repository. Successful exploitation could lead to arbitrary code execution within the context of the GitHub Actions runner. This issue affects versions up to and including v2.0.0 and was fixed in version 2.0.0.

A vulnerability in huggingface/text-generation-inference version 3.3.6 allows unauthenticated remote attackers to exploit unbounded external image fetching during input validation in VLM mode. The issue arises when the router scans inputs for Markdown image links and performs a blocking HTTP GET request, reading the entire response body into memory and cloning it before decoding. This behavior can lead to resource exhaustion, including network bandwidth saturation, memory inflation, and CPU overutilization. The vulnerability is triggered even if the request is later rejected for exceeding token limits. The default deployment configuration, which lacks memory usage limits and authentication, exacerbates the impact, potentially crashing the host machine. The issue is resolved in version 3.3.7.

A code injection vulnerability exists in the huggingface/text-generation-inference repository, specifically within the autodocs.yml workflow file. The vulnerability arises from the insecure handling of the github.head_ref user input, which is used to dynamically construct a command for installing a software package. An attacker can exploit this by forking the repository, creating a branch with a malicious payload as the name, and then opening a pull request to the base repository. Successful exploitation could lead to arbitrary code execution within the context of the GitHub Actions runner. This issue affects versions up to and including v2.0.0 and was fixed in version 2.0.0.

A vulnerability in huggingface/text-generation-inference version 3.3.6 allows unauthenticated remote attackers to exploit unbounded external image fetching during input validation in VLM mode. The issue arises when the router scans inputs for Markdown image links and performs a blocking HTTP GET request, reading the entire response body into memory and cloning it before decoding. This behavior can lead to resource exhaustion, including network bandwidth saturation, memory inflation, and CPU overutilization. The vulnerability is triggered even if the request is later rejected for exceeding token limits. The default deployment configuration, which lacks memory usage limits and authentication, exacerbates the impact, potentially crashing the host machine. The issue is resolved in version 3.3.7.

A code injection vulnerability exists in the huggingface/text-generation-inference repository, specifically within the autodocs.yml workflow file. The vulnerability arises from the insecure handling of the github.head_ref user input, which is used to dynamically construct a command for installing a software package. An attacker can exploit this by forking the repository, creating a branch with a malicious payload as the name, and then opening a pull request to the base repository. Successful exploitation could lead to arbitrary code execution within the context of the GitHub Actions runner. This issue affects versions up to and including v2.0.0 and was fixed in version 2.0.0.

A vulnerability in huggingface/text-generation-inference version 3.3.6 allows unauthenticated remote attackers to exploit unbounded external image fetching during input validation in VLM mode. The issue arises when the router scans inputs for Markdown image links and performs a blocking HTTP GET request, reading the entire response body into memory and cloning it before decoding. This behavior can lead to resource exhaustion, including network bandwidth saturation, memory inflation, and CPU overutilization. The vulnerability is triggered even if the request is later rejected for exceeding token limits. The default deployment configuration, which lacks memory usage limits and authentication, exacerbates the impact, potentially crashing the host machine. The issue is resolved in version 3.3.7.

A code injection vulnerability exists in the huggingface/text-generation-inference repository, specifically within the autodocs.yml workflow file. The vulnerability arises from the insecure handling of the github.head_ref user input, which is used to dynamically construct a command for installing a software package. An attacker can exploit this by forking the repository, creating a branch with a malicious payload as the name, and then opening a pull request to the base repository. Successful exploitation could lead to arbitrary code execution within the context of the GitHub Actions runner. This issue affects versions up to and including v2.0.0 and was fixed in version 2.0.0.

A vulnerability in huggingface/text-generation-inference version 3.3.6 allows unauthenticated remote attackers to exploit unbounded external image fetching during input validation in VLM mode. The issue arises when the router scans inputs for Markdown image links and performs a blocking HTTP GET request, reading the entire response body into memory and cloning it before decoding. This behavior can lead to resource exhaustion, including network bandwidth saturation, memory inflation, and CPU overutilization. The vulnerability is triggered even if the request is later rejected for exceeding token limits. The default deployment configuration, which lacks memory usage limits and authentication, exacerbates the impact, potentially crashing the host machine. The issue is resolved in version 3.3.7.

A code injection vulnerability exists in the huggingface/text-generation-inference repository, specifically within the autodocs.yml workflow file. The vulnerability arises from the insecure handling of the github.head_ref user input, which is used to dynamically construct a command for installing a software package. An attacker can exploit this by forking the repository, creating a branch with a malicious payload as the name, and then opening a pull request to the base repository. Successful exploitation could lead to arbitrary code execution within the context of the GitHub Actions runner. This issue affects versions up to and including v2.0.0 and was fixed in version 2.0.0.

A vulnerability in huggingface/text-generation-inference version 3.3.6 allows unauthenticated remote attackers to exploit unbounded external image fetching during input validation in VLM mode. The issue arises when the router scans inputs for Markdown image links and performs a blocking HTTP GET request, reading the entire response body into memory and cloning it before decoding. This behavior can lead to resource exhaustion, including network bandwidth saturation, memory inflation, and CPU overutilization. The vulnerability is triggered even if the request is later rejected for exceeding token limits. The default deployment configuration, which lacks memory usage limits and authentication, exacerbates the impact, potentially crashing the host machine. The issue is resolved in version 3.3.7.

A code injection vulnerability exists in the huggingface/text-generation-inference repository, specifically within the autodocs.yml workflow file. The vulnerability arises from the insecure handling of the github.head_ref user input, which is used to dynamically construct a command for installing a software package. An attacker can exploit this by forking the repository, creating a branch with a malicious payload as the name, and then opening a pull request to the base repository. Successful exploitation could lead to arbitrary code execution within the context of the GitHub Actions runner. This issue affects versions up to and including v2.0.0 and was fixed in version 2.0.0.

A vulnerability in huggingface/text-generation-inference version 3.3.6 allows unauthenticated remote attackers to exploit unbounded external image fetching during input validation in VLM mode. The issue arises when the router scans inputs for Markdown image links and performs a blocking HTTP GET request, reading the entire response body into memory and cloning it before decoding. This behavior can lead to resource exhaustion, including network bandwidth saturation, memory inflation, and CPU overutilization. The vulnerability is triggered even if the request is later rejected for exceeding token limits. The default deployment configuration, which lacks memory usage limits and authentication, exacerbates the impact, potentially crashing the host machine. The issue is resolved in version 3.3.7.

A code injection vulnerability exists in the huggingface/text-generation-inference repository, specifically within the autodocs.yml workflow file. The vulnerability arises from the insecure handling of the github.head_ref user input, which is used to dynamically construct a command for installing a software package. An attacker can exploit this by forking the repository, creating a branch with a malicious payload as the name, and then opening a pull request to the base repository. Successful exploitation could lead to arbitrary code execution within the context of the GitHub Actions runner. This issue affects versions up to and including v2.0.0 and was fixed in version 2.0.0.

A vulnerability in huggingface/text-generation-inference version 3.3.6 allows unauthenticated remote attackers to exploit unbounded external image fetching during input validation in VLM mode. The issue arises when the router scans inputs for Markdown image links and performs a blocking HTTP GET request, reading the entire response body into memory and cloning it before decoding. This behavior can lead to resource exhaustion, including network bandwidth saturation, memory inflation, and CPU overutilization. The vulnerability is triggered even if the request is later rejected for exceeding token limits. The default deployment configuration, which lacks memory usage limits and authentication, exacerbates the impact, potentially crashing the host machine. The issue is resolved in version 3.3.7.

A code injection vulnerability exists in the huggingface/text-generation-inference repository, specifically within the autodocs.yml workflow file. The vulnerability arises from the insecure handling of the github.head_ref user input, which is used to dynamically construct a command for installing a software package. An attacker can exploit this by forking the repository, creating a branch with a malicious payload as the name, and then opening a pull request to the base repository. Successful exploitation could lead to arbitrary code execution within the context of the GitHub Actions runner. This issue affects versions up to and including v2.0.0 and was fixed in version 2.0.0.

A vulnerability in huggingface/text-generation-inference version 3.3.6 allows unauthenticated remote attackers to exploit unbounded external image fetching during input validation in VLM mode. The issue arises when the router scans inputs for Markdown image links and performs a blocking HTTP GET request, reading the entire response body into memory and cloning it before decoding. This behavior can lead to resource exhaustion, including network bandwidth saturation, memory inflation, and CPU overutilization. The vulnerability is triggered even if the request is later rejected for exceeding token limits. The default deployment configuration, which lacks memory usage limits and authentication, exacerbates the impact, potentially crashing the host machine. The issue is resolved in version 3.3.7.

A code injection vulnerability exists in the huggingface/text-generation-inference repository, specifically within the autodocs.yml workflow file. The vulnerability arises from the insecure handling of the github.head_ref user input, which is used to dynamically construct a command for installing a software package. An attacker can exploit this by forking the repository, creating a branch with a malicious payload as the name, and then opening a pull request to the base repository. Successful exploitation could lead to arbitrary code execution within the context of the GitHub Actions runner. This issue affects versions up to and including v2.0.0 and was fixed in version 2.0.0.

A vulnerability in huggingface/text-generation-inference version 3.3.6 allows unauthenticated remote attackers to exploit unbounded external image fetching during input validation in VLM mode. The issue arises when the router scans inputs for Markdown image links and performs a blocking HTTP GET request, reading the entire response body into memory and cloning it before decoding. This behavior can lead to resource exhaustion, including network bandwidth saturation, memory inflation, and CPU overutilization. The vulnerability is triggered even if the request is later rejected for exceeding token limits. The default deployment configuration, which lacks memory usage limits and authentication, exacerbates the impact, potentially crashing the host machine. The issue is resolved in version 3.3.7.

A code injection vulnerability exists in the huggingface/text-generation-inference repository, specifically within the autodocs.yml workflow file. The vulnerability arises from the insecure handling of the github.head_ref user input, which is used to dynamically construct a command for installing a software package. An attacker can exploit this by forking the repository, creating a branch with a malicious payload as the name, and then opening a pull request to the base repository. Successful exploitation could lead to arbitrary code execution within the context of the GitHub Actions runner. This issue affects versions up to and including v2.0.0 and was fixed in version 2.0.0.