pyarrow

Use After Free vulnerability in Apache Arrow C++.

This issue affects Apache Arrow C++ from 15.0.0 through 23.0.0. It can be triggered when reading an Arrow IPC file (but not an IPC stream) with pre-buffering enabled, if the IPC file contains data with variadic buffers (such as Binary View and String View data). Depending on the number of variadic buffers in a record batch column and on the temporal sequence of multi-threaded IO, a write to a dangling pointer could occur. The value (a std::shared_ptr<Buffer> object) that is written to the dangling pointer is not under direct control of the attacker.

Pre-buffering is disabled by default but can be enabled using a specific C++ API call (RecordBatchFileReader::PreBufferMetadata). The functionality is not exposed in language bindings (Python, Ruby, C GLib), so these bindings are not vulnerable.

The most likely consequence of this issue would be random crashes or memory corruption when reading specific kinds of IPC files. If the application allows ingesting IPC files from untrusted sources, this could plausibly be exploited for denial of service. Inducing more targeted kinds of misbehavior (such as confidential data extraction from the running process) depends on memory allocation and multi-threaded IO temporal patterns that are unlikely to be easily controlled by an attacker.

Advice for users of Arrow C++:

1. check whether you enable pre-buffering on the IPC file reader (using RecordBatchFileReader::PreBufferMetadata)

2. if so, either disable pre-buffering (which may have adverse performance consequences), or switch to Arrow 23.0.1 which is not vulnerable

Use After Free vulnerability in Apache Arrow C++.

This issue affects Apache Arrow C++ from 15.0.0 through 23.0.0. It can be triggered when reading an Arrow IPC file (but not an IPC stream) with pre-buffering enabled, if the IPC file contains data with variadic buffers (such as Binary View and String View data). Depending on the number of variadic buffers in a record batch column and on the temporal sequence of multi-threaded IO, a write to a dangling pointer could occur. The value (a std::shared_ptr<Buffer> object) that is written to the dangling pointer is not under direct control of the attacker.

Pre-buffering is disabled by default but can be enabled using a specific C++ API call (RecordBatchFileReader::PreBufferMetadata). The functionality is not exposed in language bindings (Python, Ruby, C GLib), so these bindings are not vulnerable.

The most likely consequence of this issue would be random crashes or memory corruption when reading specific kinds of IPC files. If the application allows ingesting IPC files from untrusted sources, this could plausibly be exploited for denial of service. Inducing more targeted kinds of misbehavior (such as confidential data extraction from the running process) depends on memory allocation and multi-threaded IO temporal patterns that are unlikely to be easily controlled by an attacker.

Advice for users of Arrow C++:

1. check whether you enable pre-buffering on the IPC file reader (using RecordBatchFileReader::PreBufferMetadata)

2. if so, either disable pre-buffering (which may have adverse performance consequences), or switch to Arrow 23.0.1 which is not vulnerable

Use After Free vulnerability in Apache Arrow C++.

This issue affects Apache Arrow C++ from 15.0.0 through 23.0.0. It can be triggered when reading an Arrow IPC file (but not an IPC stream) with pre-buffering enabled, if the IPC file contains data with variadic buffers (such as Binary View and String View data). Depending on the number of variadic buffers in a record batch column and on the temporal sequence of multi-threaded IO, a write to a dangling pointer could occur. The value (a std::shared_ptr<Buffer> object) that is written to the dangling pointer is not under direct control of the attacker.

Pre-buffering is disabled by default but can be enabled using a specific C++ API call (RecordBatchFileReader::PreBufferMetadata). The functionality is not exposed in language bindings (Python, Ruby, C GLib), so these bindings are not vulnerable.

The most likely consequence of this issue would be random crashes or memory corruption when reading specific kinds of IPC files. If the application allows ingesting IPC files from untrusted sources, this could plausibly be exploited for denial of service. Inducing more targeted kinds of misbehavior (such as confidential data extraction from the running process) depends on memory allocation and multi-threaded IO temporal patterns that are unlikely to be easily controlled by an attacker.

Advice for users of Arrow C++:

1. check whether you enable pre-buffering on the IPC file reader (using RecordBatchFileReader::PreBufferMetadata)

2. if so, either disable pre-buffering (which may have adverse performance consequences), or switch to Arrow 23.0.1 which is not vulnerable

Use After Free vulnerability in Apache Arrow C++.

This issue affects Apache Arrow C++ from 15.0.0 through 23.0.0. It can be triggered when reading an Arrow IPC file (but not an IPC stream) with pre-buffering enabled, if the IPC file contains data with variadic buffers (such as Binary View and String View data). Depending on the number of variadic buffers in a record batch column and on the temporal sequence of multi-threaded IO, a write to a dangling pointer could occur. The value (a std::shared_ptr<Buffer> object) that is written to the dangling pointer is not under direct control of the attacker.

Pre-buffering is disabled by default but can be enabled using a specific C++ API call (RecordBatchFileReader::PreBufferMetadata). The functionality is not exposed in language bindings (Python, Ruby, C GLib), so these bindings are not vulnerable.

The most likely consequence of this issue would be random crashes or memory corruption when reading specific kinds of IPC files. If the application allows ingesting IPC files from untrusted sources, this could plausibly be exploited for denial of service. Inducing more targeted kinds of misbehavior (such as confidential data extraction from the running process) depends on memory allocation and multi-threaded IO temporal patterns that are unlikely to be easily controlled by an attacker.

Advice for users of Arrow C++:

1. check whether you enable pre-buffering on the IPC file reader (using RecordBatchFileReader::PreBufferMetadata)

2. if so, either disable pre-buffering (which may have adverse performance consequences), or switch to Arrow 23.0.1 which is not vulnerable

Use After Free vulnerability in Apache Arrow C++.

This issue affects Apache Arrow C++ from 15.0.0 through 23.0.0. It can be triggered when reading an Arrow IPC file (but not an IPC stream) with pre-buffering enabled, if the IPC file contains data with variadic buffers (such as Binary View and String View data). Depending on the number of variadic buffers in a record batch column and on the temporal sequence of multi-threaded IO, a write to a dangling pointer could occur. The value (a std::shared_ptr<Buffer> object) that is written to the dangling pointer is not under direct control of the attacker.

Pre-buffering is disabled by default but can be enabled using a specific C++ API call (RecordBatchFileReader::PreBufferMetadata). The functionality is not exposed in language bindings (Python, Ruby, C GLib), so these bindings are not vulnerable.

The most likely consequence of this issue would be random crashes or memory corruption when reading specific kinds of IPC files. If the application allows ingesting IPC files from untrusted sources, this could plausibly be exploited for denial of service. Inducing more targeted kinds of misbehavior (such as confidential data extraction from the running process) depends on memory allocation and multi-threaded IO temporal patterns that are unlikely to be easily controlled by an attacker.

Advice for users of Arrow C++:

1. check whether you enable pre-buffering on the IPC file reader (using RecordBatchFileReader::PreBufferMetadata)

2. if so, either disable pre-buffering (which may have adverse performance consequences), or switch to Arrow 23.0.1 which is not vulnerable

Use After Free vulnerability in Apache Arrow C++.

This issue affects Apache Arrow C++ from 15.0.0 through 23.0.0. It can be triggered when reading an Arrow IPC file (but not an IPC stream) with pre-buffering enabled, if the IPC file contains data with variadic buffers (such as Binary View and String View data). Depending on the number of variadic buffers in a record batch column and on the temporal sequence of multi-threaded IO, a write to a dangling pointer could occur. The value (a std::shared_ptr<Buffer> object) that is written to the dangling pointer is not under direct control of the attacker.

Pre-buffering is disabled by default but can be enabled using a specific C++ API call (RecordBatchFileReader::PreBufferMetadata). The functionality is not exposed in language bindings (Python, Ruby, C GLib), so these bindings are not vulnerable.

The most likely consequence of this issue would be random crashes or memory corruption when reading specific kinds of IPC files. If the application allows ingesting IPC files from untrusted sources, this could plausibly be exploited for denial of service. Inducing more targeted kinds of misbehavior (such as confidential data extraction from the running process) depends on memory allocation and multi-threaded IO temporal patterns that are unlikely to be easily controlled by an attacker.

Advice for users of Arrow C++:

1. check whether you enable pre-buffering on the IPC file reader (using RecordBatchFileReader::PreBufferMetadata)

2. if so, either disable pre-buffering (which may have adverse performance consequences), or switch to Arrow 23.0.1 which is not vulnerable

Use After Free vulnerability in Apache Arrow C++.

This issue affects Apache Arrow C++ from 15.0.0 through 23.0.0. It can be triggered when reading an Arrow IPC file (but not an IPC stream) with pre-buffering enabled, if the IPC file contains data with variadic buffers (such as Binary View and String View data). Depending on the number of variadic buffers in a record batch column and on the temporal sequence of multi-threaded IO, a write to a dangling pointer could occur. The value (a std::shared_ptr<Buffer> object) that is written to the dangling pointer is not under direct control of the attacker.

Pre-buffering is disabled by default but can be enabled using a specific C++ API call (RecordBatchFileReader::PreBufferMetadata). The functionality is not exposed in language bindings (Python, Ruby, C GLib), so these bindings are not vulnerable.

The most likely consequence of this issue would be random crashes or memory corruption when reading specific kinds of IPC files. If the application allows ingesting IPC files from untrusted sources, this could plausibly be exploited for denial of service. Inducing more targeted kinds of misbehavior (such as confidential data extraction from the running process) depends on memory allocation and multi-threaded IO temporal patterns that are unlikely to be easily controlled by an attacker.

Advice for users of Arrow C++:

1. check whether you enable pre-buffering on the IPC file reader (using RecordBatchFileReader::PreBufferMetadata)

2. if so, either disable pre-buffering (which may have adverse performance consequences), or switch to Arrow 23.0.1 which is not vulnerable

Use After Free vulnerability in Apache Arrow C++.

This issue affects Apache Arrow C++ from 15.0.0 through 23.0.0. It can be triggered when reading an Arrow IPC file (but not an IPC stream) with pre-buffering enabled, if the IPC file contains data with variadic buffers (such as Binary View and String View data). Depending on the number of variadic buffers in a record batch column and on the temporal sequence of multi-threaded IO, a write to a dangling pointer could occur. The value (a std::shared_ptr<Buffer> object) that is written to the dangling pointer is not under direct control of the attacker.

Pre-buffering is disabled by default but can be enabled using a specific C++ API call (RecordBatchFileReader::PreBufferMetadata). The functionality is not exposed in language bindings (Python, Ruby, C GLib), so these bindings are not vulnerable.

The most likely consequence of this issue would be random crashes or memory corruption when reading specific kinds of IPC files. If the application allows ingesting IPC files from untrusted sources, this could plausibly be exploited for denial of service. Inducing more targeted kinds of misbehavior (such as confidential data extraction from the running process) depends on memory allocation and multi-threaded IO temporal patterns that are unlikely to be easily controlled by an attacker.

Advice for users of Arrow C++:

1. check whether you enable pre-buffering on the IPC file reader (using RecordBatchFileReader::PreBufferMetadata)

2. if so, either disable pre-buffering (which may have adverse performance consequences), or switch to Arrow 23.0.1 which is not vulnerable

Use After Free vulnerability in Apache Arrow C++.

This issue affects Apache Arrow C++ from 15.0.0 through 23.0.0. It can be triggered when reading an Arrow IPC file (but not an IPC stream) with pre-buffering enabled, if the IPC file contains data with variadic buffers (such as Binary View and String View data). Depending on the number of variadic buffers in a record batch column and on the temporal sequence of multi-threaded IO, a write to a dangling pointer could occur. The value (a std::shared_ptr<Buffer> object) that is written to the dangling pointer is not under direct control of the attacker.

Pre-buffering is disabled by default but can be enabled using a specific C++ API call (RecordBatchFileReader::PreBufferMetadata). The functionality is not exposed in language bindings (Python, Ruby, C GLib), so these bindings are not vulnerable.

The most likely consequence of this issue would be random crashes or memory corruption when reading specific kinds of IPC files. If the application allows ingesting IPC files from untrusted sources, this could plausibly be exploited for denial of service. Inducing more targeted kinds of misbehavior (such as confidential data extraction from the running process) depends on memory allocation and multi-threaded IO temporal patterns that are unlikely to be easily controlled by an attacker.

Advice for users of Arrow C++:

1. check whether you enable pre-buffering on the IPC file reader (using RecordBatchFileReader::PreBufferMetadata)

2. if so, either disable pre-buffering (which may have adverse performance consequences), or switch to Arrow 23.0.1 which is not vulnerable

Use After Free vulnerability in Apache Arrow C++.

This issue affects Apache Arrow C++ from 15.0.0 through 23.0.0. It can be triggered when reading an Arrow IPC file (but not an IPC stream) with pre-buffering enabled, if the IPC file contains data with variadic buffers (such as Binary View and String View data). Depending on the number of variadic buffers in a record batch column and on the temporal sequence of multi-threaded IO, a write to a dangling pointer could occur. The value (a std::shared_ptr<Buffer> object) that is written to the dangling pointer is not under direct control of the attacker.

Pre-buffering is disabled by default but can be enabled using a specific C++ API call (RecordBatchFileReader::PreBufferMetadata). The functionality is not exposed in language bindings (Python, Ruby, C GLib), so these bindings are not vulnerable.

The most likely consequence of this issue would be random crashes or memory corruption when reading specific kinds of IPC files. If the application allows ingesting IPC files from untrusted sources, this could plausibly be exploited for denial of service. Inducing more targeted kinds of misbehavior (such as confidential data extraction from the running process) depends on memory allocation and multi-threaded IO temporal patterns that are unlikely to be easily controlled by an attacker.

Advice for users of Arrow C++:

1. check whether you enable pre-buffering on the IPC file reader (using RecordBatchFileReader::PreBufferMetadata)

2. if so, either disable pre-buffering (which may have adverse performance consequences), or switch to Arrow 23.0.1 which is not vulnerable

Deserialization of untrusted data in IPC and Parquet readers in the Apache Arrow R package versions 4.0.0 through 16.1.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example, user-supplied input files). This vulnerability only affects the arrow R package, not other Apache Arrow implementations or bindings unless those bindings are specifically used via the R package (for example, an R application that embeds a Python interpreter and uses PyArrow to read files from untrusted sources is still vulnerable if the arrow R package is an affected version). It is recommended that users of the arrow R package upgrade to 17.0.0 or later. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to arrow 17.0.0 or later. If using an affected version of the package, untrusted data can read into a Table and its internal to_data_frame() method can be used as a workaround (e.g., read_parquet(..., as_data_frame = FALSE)$to_data_frame()).

This issue affects the Apache Arrow R package: from 4.0.0 through 16.1.0.

Users are recommended to upgrade to version 17.0.0, which fixes the issue.

Use After Free vulnerability in Apache Arrow C++.

This issue affects Apache Arrow C++ from 15.0.0 through 23.0.0. It can be triggered when reading an Arrow IPC file (but not an IPC stream) with pre-buffering enabled, if the IPC file contains data with variadic buffers (such as Binary View and String View data). Depending on the number of variadic buffers in a record batch column and on the temporal sequence of multi-threaded IO, a write to a dangling pointer could occur. The value (a std::shared_ptr<Buffer> object) that is written to the dangling pointer is not under direct control of the attacker.

Pre-buffering is disabled by default but can be enabled using a specific C++ API call (RecordBatchFileReader::PreBufferMetadata). The functionality is not exposed in language bindings (Python, Ruby, C GLib), so these bindings are not vulnerable.

The most likely consequence of this issue would be random crashes or memory corruption when reading specific kinds of IPC files. If the application allows ingesting IPC files from untrusted sources, this could plausibly be exploited for denial of service. Inducing more targeted kinds of misbehavior (such as confidential data extraction from the running process) depends on memory allocation and multi-threaded IO temporal patterns that are unlikely to be easily controlled by an attacker.

Advice for users of Arrow C++:

1. check whether you enable pre-buffering on the IPC file reader (using RecordBatchFileReader::PreBufferMetadata)

2. if so, either disable pre-buffering (which may have adverse performance consequences), or switch to Arrow 23.0.1 which is not vulnerable

Deserialization of untrusted data in IPC and Parquet readers in the Apache Arrow R package versions 4.0.0 through 16.1.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example, user-supplied input files). This vulnerability only affects the arrow R package, not other Apache Arrow implementations or bindings unless those bindings are specifically used via the R package (for example, an R application that embeds a Python interpreter and uses PyArrow to read files from untrusted sources is still vulnerable if the arrow R package is an affected version). It is recommended that users of the arrow R package upgrade to 17.0.0 or later. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to arrow 17.0.0 or later. If using an affected version of the package, untrusted data can read into a Table and its internal to_data_frame() method can be used as a workaround (e.g., read_parquet(..., as_data_frame = FALSE)$to_data_frame()).

This issue affects the Apache Arrow R package: from 4.0.0 through 16.1.0.

Users are recommended to upgrade to version 17.0.0, which fixes the issue.

Use After Free vulnerability in Apache Arrow C++.

This issue affects Apache Arrow C++ from 15.0.0 through 23.0.0. It can be triggered when reading an Arrow IPC file (but not an IPC stream) with pre-buffering enabled, if the IPC file contains data with variadic buffers (such as Binary View and String View data). Depending on the number of variadic buffers in a record batch column and on the temporal sequence of multi-threaded IO, a write to a dangling pointer could occur. The value (a std::shared_ptr<Buffer> object) that is written to the dangling pointer is not under direct control of the attacker.

Pre-buffering is disabled by default but can be enabled using a specific C++ API call (RecordBatchFileReader::PreBufferMetadata). The functionality is not exposed in language bindings (Python, Ruby, C GLib), so these bindings are not vulnerable.

The most likely consequence of this issue would be random crashes or memory corruption when reading specific kinds of IPC files. If the application allows ingesting IPC files from untrusted sources, this could plausibly be exploited for denial of service. Inducing more targeted kinds of misbehavior (such as confidential data extraction from the running process) depends on memory allocation and multi-threaded IO temporal patterns that are unlikely to be easily controlled by an attacker.

Advice for users of Arrow C++:

1. check whether you enable pre-buffering on the IPC file reader (using RecordBatchFileReader::PreBufferMetadata)

2. if so, either disable pre-buffering (which may have adverse performance consequences), or switch to Arrow 23.0.1 which is not vulnerable

Deserialization of untrusted data in IPC and Parquet readers in the Apache Arrow R package versions 4.0.0 through 16.1.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example, user-supplied input files). This vulnerability only affects the arrow R package, not other Apache Arrow implementations or bindings unless those bindings are specifically used via the R package (for example, an R application that embeds a Python interpreter and uses PyArrow to read files from untrusted sources is still vulnerable if the arrow R package is an affected version). It is recommended that users of the arrow R package upgrade to 17.0.0 or later. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to arrow 17.0.0 or later. If using an affected version of the package, untrusted data can read into a Table and its internal to_data_frame() method can be used as a workaround (e.g., read_parquet(..., as_data_frame = FALSE)$to_data_frame()).

This issue affects the Apache Arrow R package: from 4.0.0 through 16.1.0.

Users are recommended to upgrade to version 17.0.0, which fixes the issue.

Use After Free vulnerability in Apache Arrow C++.

This issue affects Apache Arrow C++ from 15.0.0 through 23.0.0. It can be triggered when reading an Arrow IPC file (but not an IPC stream) with pre-buffering enabled, if the IPC file contains data with variadic buffers (such as Binary View and String View data). Depending on the number of variadic buffers in a record batch column and on the temporal sequence of multi-threaded IO, a write to a dangling pointer could occur. The value (a std::shared_ptr<Buffer> object) that is written to the dangling pointer is not under direct control of the attacker.

Pre-buffering is disabled by default but can be enabled using a specific C++ API call (RecordBatchFileReader::PreBufferMetadata). The functionality is not exposed in language bindings (Python, Ruby, C GLib), so these bindings are not vulnerable.

The most likely consequence of this issue would be random crashes or memory corruption when reading specific kinds of IPC files. If the application allows ingesting IPC files from untrusted sources, this could plausibly be exploited for denial of service. Inducing more targeted kinds of misbehavior (such as confidential data extraction from the running process) depends on memory allocation and multi-threaded IO temporal patterns that are unlikely to be easily controlled by an attacker.

Advice for users of Arrow C++:

1. check whether you enable pre-buffering on the IPC file reader (using RecordBatchFileReader::PreBufferMetadata)

2. if so, either disable pre-buffering (which may have adverse performance consequences), or switch to Arrow 23.0.1 which is not vulnerable

Deserialization of untrusted data in IPC and Parquet readers in the Apache Arrow R package versions 4.0.0 through 16.1.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example, user-supplied input files). This vulnerability only affects the arrow R package, not other Apache Arrow implementations or bindings unless those bindings are specifically used via the R package (for example, an R application that embeds a Python interpreter and uses PyArrow to read files from untrusted sources is still vulnerable if the arrow R package is an affected version). It is recommended that users of the arrow R package upgrade to 17.0.0 or later. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to arrow 17.0.0 or later. If using an affected version of the package, untrusted data can read into a Table and its internal to_data_frame() method can be used as a workaround (e.g., read_parquet(..., as_data_frame = FALSE)$to_data_frame()).

This issue affects the Apache Arrow R package: from 4.0.0 through 16.1.0.

Users are recommended to upgrade to version 17.0.0, which fixes the issue.

Use After Free vulnerability in Apache Arrow C++.

This issue affects Apache Arrow C++ from 15.0.0 through 23.0.0. It can be triggered when reading an Arrow IPC file (but not an IPC stream) with pre-buffering enabled, if the IPC file contains data with variadic buffers (such as Binary View and String View data). Depending on the number of variadic buffers in a record batch column and on the temporal sequence of multi-threaded IO, a write to a dangling pointer could occur. The value (a std::shared_ptr<Buffer> object) that is written to the dangling pointer is not under direct control of the attacker.

Pre-buffering is disabled by default but can be enabled using a specific C++ API call (RecordBatchFileReader::PreBufferMetadata). The functionality is not exposed in language bindings (Python, Ruby, C GLib), so these bindings are not vulnerable.

The most likely consequence of this issue would be random crashes or memory corruption when reading specific kinds of IPC files. If the application allows ingesting IPC files from untrusted sources, this could plausibly be exploited for denial of service. Inducing more targeted kinds of misbehavior (such as confidential data extraction from the running process) depends on memory allocation and multi-threaded IO temporal patterns that are unlikely to be easily controlled by an attacker.

Advice for users of Arrow C++:

1. check whether you enable pre-buffering on the IPC file reader (using RecordBatchFileReader::PreBufferMetadata)

2. if so, either disable pre-buffering (which may have adverse performance consequences), or switch to Arrow 23.0.1 which is not vulnerable

Deserialization of untrusted data in IPC and Parquet readers in the Apache Arrow R package versions 4.0.0 through 16.1.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example, user-supplied input files). This vulnerability only affects the arrow R package, not other Apache Arrow implementations or bindings unless those bindings are specifically used via the R package (for example, an R application that embeds a Python interpreter and uses PyArrow to read files from untrusted sources is still vulnerable if the arrow R package is an affected version). It is recommended that users of the arrow R package upgrade to 17.0.0 or later. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to arrow 17.0.0 or later. If using an affected version of the package, untrusted data can read into a Table and its internal to_data_frame() method can be used as a workaround (e.g., read_parquet(..., as_data_frame = FALSE)$to_data_frame()).

This issue affects the Apache Arrow R package: from 4.0.0 through 16.1.0.

Users are recommended to upgrade to version 17.0.0, which fixes the issue.

Deserialization of untrusted data in IPC and Parquet readers in the Apache Arrow R package versions 4.0.0 through 16.1.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example, user-supplied input files). This vulnerability only affects the arrow R package, not other Apache Arrow implementations or bindings unless those bindings are specifically used via the R package (for example, an R application that embeds a Python interpreter and uses PyArrow to read files from untrusted sources is still vulnerable if the arrow R package is an affected version). It is recommended that users of the arrow R package upgrade to 17.0.0 or later. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to arrow 17.0.0 or later. If using an affected version of the package, untrusted data can read into a Table and its internal to_data_frame() method can be used as a workaround (e.g., read_parquet(..., as_data_frame = FALSE)$to_data_frame()).

This issue affects the Apache Arrow R package: from 4.0.0 through 16.1.0.

Users are recommended to upgrade to version 17.0.0, which fixes the issue.

Deserialization of untrusted data in IPC and Parquet readers in the Apache Arrow R package versions 4.0.0 through 16.1.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example, user-supplied input files). This vulnerability only affects the arrow R package, not other Apache Arrow implementations or bindings unless those bindings are specifically used via the R package (for example, an R application that embeds a Python interpreter and uses PyArrow to read files from untrusted sources is still vulnerable if the arrow R package is an affected version). It is recommended that users of the arrow R package upgrade to 17.0.0 or later. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to arrow 17.0.0 or later. If using an affected version of the package, untrusted data can read into a Table and its internal to_data_frame() method can be used as a workaround (e.g., read_parquet(..., as_data_frame = FALSE)$to_data_frame()).

This issue affects the Apache Arrow R package: from 4.0.0 through 16.1.0.

Users are recommended to upgrade to version 17.0.0, which fixes the issue.

Deserialization of untrusted data in IPC and Parquet readers in the Apache Arrow R package versions 4.0.0 through 16.1.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example, user-supplied input files). This vulnerability only affects the arrow R package, not other Apache Arrow implementations or bindings unless those bindings are specifically used via the R package (for example, an R application that embeds a Python interpreter and uses PyArrow to read files from untrusted sources is still vulnerable if the arrow R package is an affected version). It is recommended that users of the arrow R package upgrade to 17.0.0 or later. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to arrow 17.0.0 or later. If using an affected version of the package, untrusted data can read into a Table and its internal to_data_frame() method can be used as a workaround (e.g., read_parquet(..., as_data_frame = FALSE)$to_data_frame()).

This issue affects the Apache Arrow R package: from 4.0.0 through 16.1.0.

Users are recommended to upgrade to version 17.0.0, which fixes the issue.

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files).

Deserialization of untrusted data in IPC and Parquet readers in the Apache Arrow R package versions 4.0.0 through 16.1.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example, user-supplied input files). This vulnerability only affects the arrow R package, not other Apache Arrow implementations or bindings unless those bindings are specifically used via the R package (for example, an R application that embeds a Python interpreter and uses PyArrow to read files from untrusted sources is still vulnerable if the arrow R package is an affected version). It is recommended that users of the arrow R package upgrade to 17.0.0 or later. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to arrow 17.0.0 or later. If using an affected version of the package, untrusted data can read into a Table and its internal to_data_frame() method can be used as a workaround (e.g., read_parquet(..., as_data_frame = FALSE)$to_data_frame()).

This issue affects the Apache Arrow R package: from 4.0.0 through 16.1.0.

Users are recommended to upgrade to version 17.0.0, which fixes the issue.

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files).

Deserialization of untrusted data in IPC and Parquet readers in the Apache Arrow R package versions 4.0.0 through 16.1.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example, user-supplied input files). This vulnerability only affects the arrow R package, not other Apache Arrow implementations or bindings unless those bindings are specifically used via the R package (for example, an R application that embeds a Python interpreter and uses PyArrow to read files from untrusted sources is still vulnerable if the arrow R package is an affected version). It is recommended that users of the arrow R package upgrade to 17.0.0 or later. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to arrow 17.0.0 or later. If using an affected version of the package, untrusted data can read into a Table and its internal to_data_frame() method can be used as a workaround (e.g., read_parquet(..., as_data_frame = FALSE)$to_data_frame()).

This issue affects the Apache Arrow R package: from 4.0.0 through 16.1.0.

Users are recommended to upgrade to version 17.0.0, which fixes the issue.

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files).

Deserialization of untrusted data in IPC and Parquet readers in the Apache Arrow R package versions 4.0.0 through 16.1.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example, user-supplied input files). This vulnerability only affects the arrow R package, not other Apache Arrow implementations or bindings unless those bindings are specifically used via the R package (for example, an R application that embeds a Python interpreter and uses PyArrow to read files from untrusted sources is still vulnerable if the arrow R package is an affected version). It is recommended that users of the arrow R package upgrade to 17.0.0 or later. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to arrow 17.0.0 or later. If using an affected version of the package, untrusted data can read into a Table and its internal to_data_frame() method can be used as a workaround (e.g., read_parquet(..., as_data_frame = FALSE)$to_data_frame()).

This issue affects the Apache Arrow R package: from 4.0.0 through 16.1.0.

Users are recommended to upgrade to version 17.0.0, which fixes the issue.

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files).

Deserialization of untrusted data in IPC and Parquet readers in the Apache Arrow R package versions 4.0.0 through 16.1.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example, user-supplied input files). This vulnerability only affects the arrow R package, not other Apache Arrow implementations or bindings unless those bindings are specifically used via the R package (for example, an R application that embeds a Python interpreter and uses PyArrow to read files from untrusted sources is still vulnerable if the arrow R package is an affected version). It is recommended that users of the arrow R package upgrade to 17.0.0 or later. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to arrow 17.0.0 or later. If using an affected version of the package, untrusted data can read into a Table and its internal to_data_frame() method can be used as a workaround (e.g., read_parquet(..., as_data_frame = FALSE)$to_data_frame()).

This issue affects the Apache Arrow R package: from 4.0.0 through 16.1.0.

Users are recommended to upgrade to version 17.0.0, which fixes the issue.

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files).

Deserialization of untrusted data in IPC and Parquet readers in the Apache Arrow R package versions 4.0.0 through 16.1.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example, user-supplied input files). This vulnerability only affects the arrow R package, not other Apache Arrow implementations or bindings unless those bindings are specifically used via the R package (for example, an R application that embeds a Python interpreter and uses PyArrow to read files from untrusted sources is still vulnerable if the arrow R package is an affected version). It is recommended that users of the arrow R package upgrade to 17.0.0 or later. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to arrow 17.0.0 or later. If using an affected version of the package, untrusted data can read into a Table and its internal to_data_frame() method can be used as a workaround (e.g., read_parquet(..., as_data_frame = FALSE)$to_data_frame()).

This issue affects the Apache Arrow R package: from 4.0.0 through 16.1.0.

Users are recommended to upgrade to version 17.0.0, which fixes the issue.

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files).

Deserialization of untrusted data in IPC and Parquet readers in the Apache Arrow R package versions 4.0.0 through 16.1.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example, user-supplied input files). This vulnerability only affects the arrow R package, not other Apache Arrow implementations or bindings unless those bindings are specifically used via the R package (for example, an R application that embeds a Python interpreter and uses PyArrow to read files from untrusted sources is still vulnerable if the arrow R package is an affected version). It is recommended that users of the arrow R package upgrade to 17.0.0 or later. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to arrow 17.0.0 or later. If using an affected version of the package, untrusted data can read into a Table and its internal to_data_frame() method can be used as a workaround (e.g., read_parquet(..., as_data_frame = FALSE)$to_data_frame()).

This issue affects the Apache Arrow R package: from 4.0.0 through 16.1.0.

Users are recommended to upgrade to version 17.0.0, which fixes the issue.

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files).

Deserialization of untrusted data in IPC and Parquet readers in the Apache Arrow R package versions 4.0.0 through 16.1.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example, user-supplied input files). This vulnerability only affects the arrow R package, not other Apache Arrow implementations or bindings unless those bindings are specifically used via the R package (for example, an R application that embeds a Python interpreter and uses PyArrow to read files from untrusted sources is still vulnerable if the arrow R package is an affected version). It is recommended that users of the arrow R package upgrade to 17.0.0 or later. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to arrow 17.0.0 or later. If using an affected version of the package, untrusted data can read into a Table and its internal to_data_frame() method can be used as a workaround (e.g., read_parquet(..., as_data_frame = FALSE)$to_data_frame()).

This issue affects the Apache Arrow R package: from 4.0.0 through 16.1.0.

Users are recommended to upgrade to version 17.0.0, which fixes the issue.

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files).

Deserialization of untrusted data in IPC and Parquet readers in the Apache Arrow R package versions 4.0.0 through 16.1.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example, user-supplied input files). This vulnerability only affects the arrow R package, not other Apache Arrow implementations or bindings unless those bindings are specifically used via the R package (for example, an R application that embeds a Python interpreter and uses PyArrow to read files from untrusted sources is still vulnerable if the arrow R package is an affected version). It is recommended that users of the arrow R package upgrade to 17.0.0 or later. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to arrow 17.0.0 or later. If using an affected version of the package, untrusted data can read into a Table and its internal to_data_frame() method can be used as a workaround (e.g., read_parquet(..., as_data_frame = FALSE)$to_data_frame()).

This issue affects the Apache Arrow R package: from 4.0.0 through 16.1.0.

Users are recommended to upgrade to version 17.0.0, which fixes the issue.

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files).

Deserialization of untrusted data in IPC and Parquet readers in the Apache Arrow R package versions 4.0.0 through 16.1.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example, user-supplied input files). This vulnerability only affects the arrow R package, not other Apache Arrow implementations or bindings unless those bindings are specifically used via the R package (for example, an R application that embeds a Python interpreter and uses PyArrow to read files from untrusted sources is still vulnerable if the arrow R package is an affected version). It is recommended that users of the arrow R package upgrade to 17.0.0 or later. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to arrow 17.0.0 or later. If using an affected version of the package, untrusted data can read into a Table and its internal to_data_frame() method can be used as a workaround (e.g., read_parquet(..., as_data_frame = FALSE)$to_data_frame()).

This issue affects the Apache Arrow R package: from 4.0.0 through 16.1.0.

Users are recommended to upgrade to version 17.0.0, which fixes the issue.

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files).

Deserialization of untrusted data in IPC and Parquet readers in the Apache Arrow R package versions 4.0.0 through 16.1.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example, user-supplied input files). This vulnerability only affects the arrow R package, not other Apache Arrow implementations or bindings unless those bindings are specifically used via the R package (for example, an R application that embeds a Python interpreter and uses PyArrow to read files from untrusted sources is still vulnerable if the arrow R package is an affected version). It is recommended that users of the arrow R package upgrade to 17.0.0 or later. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to arrow 17.0.0 or later. If using an affected version of the package, untrusted data can read into a Table and its internal to_data_frame() method can be used as a workaround (e.g., read_parquet(..., as_data_frame = FALSE)$to_data_frame()).

This issue affects the Apache Arrow R package: from 4.0.0 through 16.1.0.

Users are recommended to upgrade to version 17.0.0, which fixes the issue.

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files).

Deserialization of untrusted data in IPC and Parquet readers in the Apache Arrow R package versions 4.0.0 through 16.1.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example, user-supplied input files). This vulnerability only affects the arrow R package, not other Apache Arrow implementations or bindings unless those bindings are specifically used via the R package (for example, an R application that embeds a Python interpreter and uses PyArrow to read files from untrusted sources is still vulnerable if the arrow R package is an affected version). It is recommended that users of the arrow R package upgrade to 17.0.0 or later. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to arrow 17.0.0 or later. If using an affected version of the package, untrusted data can read into a Table and its internal to_data_frame() method can be used as a workaround (e.g., read_parquet(..., as_data_frame = FALSE)$to_data_frame()).

This issue affects the Apache Arrow R package: from 4.0.0 through 16.1.0.

Users are recommended to upgrade to version 17.0.0, which fixes the issue.

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files).

Deserialization of untrusted data in IPC and Parquet readers in the Apache Arrow R package versions 4.0.0 through 16.1.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example, user-supplied input files). This vulnerability only affects the arrow R package, not other Apache Arrow implementations or bindings unless those bindings are specifically used via the R package (for example, an R application that embeds a Python interpreter and uses PyArrow to read files from untrusted sources is still vulnerable if the arrow R package is an affected version). It is recommended that users of the arrow R package upgrade to 17.0.0 or later. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to arrow 17.0.0 or later. If using an affected version of the package, untrusted data can read into a Table and its internal to_data_frame() method can be used as a workaround (e.g., read_parquet(..., as_data_frame = FALSE)$to_data_frame()).

This issue affects the Apache Arrow R package: from 4.0.0 through 16.1.0.

Users are recommended to upgrade to version 17.0.0, which fixes the issue.

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files).

Deserialization of untrusted data in IPC and Parquet readers in the Apache Arrow R package versions 4.0.0 through 16.1.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example, user-supplied input files). This vulnerability only affects the arrow R package, not other Apache Arrow implementations or bindings unless those bindings are specifically used via the R package (for example, an R application that embeds a Python interpreter and uses PyArrow to read files from untrusted sources is still vulnerable if the arrow R package is an affected version). It is recommended that users of the arrow R package upgrade to 17.0.0 or later. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to arrow 17.0.0 or later. If using an affected version of the package, untrusted data can read into a Table and its internal to_data_frame() method can be used as a workaround (e.g., read_parquet(..., as_data_frame = FALSE)$to_data_frame()).

This issue affects the Apache Arrow R package: from 4.0.0 through 16.1.0.

Users are recommended to upgrade to version 17.0.0, which fixes the issue.

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files).

Deserialization of untrusted data in IPC and Parquet readers in the Apache Arrow R package versions 4.0.0 through 16.1.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example, user-supplied input files). This vulnerability only affects the arrow R package, not other Apache Arrow implementations or bindings unless those bindings are specifically used via the R package (for example, an R application that embeds a Python interpreter and uses PyArrow to read files from untrusted sources is still vulnerable if the arrow R package is an affected version). It is recommended that users of the arrow R package upgrade to 17.0.0 or later. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to arrow 17.0.0 or later. If using an affected version of the package, untrusted data can read into a Table and its internal to_data_frame() method can be used as a workaround (e.g., read_parquet(..., as_data_frame = FALSE)$to_data_frame()).

This issue affects the Apache Arrow R package: from 4.0.0 through 16.1.0.

Users are recommended to upgrade to version 17.0.0, which fixes the issue.

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files).

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files).

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files).

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files).

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files).

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files).

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files).

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files).

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files).

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files).

While investigating UBSAN errors in https://github.com/apache/arrow/pull/5365 it was discovered Apache Arrow versions 0.12.0 to 0.14.1, left memory Array data uninitialized when reading RLE null data from parquet. This affected the C++, Python, Ruby and R implementations. The uninitialized memory could potentially be shared if are transmitted over the wire (for instance with Flight) or persisted in the streaming IPC and file formats.

It was discovered that the C++ implementation (which underlies the R, Python and Ruby implementations) of Apache Arrow 0.14.0 to 0.14.1 had a uninitialized memory bug when building arrays with null values in some cases. This can lead to uninitialized memory being unintentionally shared if Arrow Arrays are transmitted over the wire (for instance with Flight) or persisted in the streaming IPC and file formats.

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files).

While investigating UBSAN errors in https://github.com/apache/arrow/pull/5365 it was discovered Apache Arrow versions 0.12.0 to 0.14.1, left memory Array data uninitialized when reading RLE null data from parquet. This affected the C++, Python, Ruby and R implementations. The uninitialized memory could potentially be shared if are transmitted over the wire (for instance with Flight) or persisted in the streaming IPC and file formats.

It was discovered that the C++ implementation (which underlies the R, Python and Ruby implementations) of Apache Arrow 0.14.0 to 0.14.1 had a uninitialized memory bug when building arrays with null values in some cases. This can lead to uninitialized memory being unintentionally shared if Arrow Arrays are transmitted over the wire (for instance with Flight) or persisted in the streaming IPC and file formats.

Deserialization of untrusted data in IPC and Parquet readers in PyArrow versions 0.14.0 to 14.0.0 allows arbitrary code execution. An application is vulnerable if it reads Arrow IPC, Feather or Parquet data from untrusted sources (for example user-supplied input files).

While investigating UBSAN errors in https://github.com/apache/arrow/pull/5365 it was discovered Apache Arrow versions 0.12.0 to 0.14.1, left memory Array data uninitialized when reading RLE null data from parquet. This affected the C++, Python, Ruby and R implementations. The uninitialized memory could potentially be shared if are transmitted over the wire (for instance with Flight) or persisted in the streaming IPC and file formats.

It was discovered that the C++ implementation (which underlies the R, Python and Ruby implementations) of Apache Arrow 0.14.0 to 0.14.1 had a uninitialized memory bug when building arrays with null values in some cases. This can lead to uninitialized memory being unintentionally shared if Arrow Arrays are transmitted over the wire (for instance with Flight) or persisted in the streaming IPC and file formats.

While investigating UBSAN errors in https://github.com/apache/arrow/pull/5365 it was discovered Apache Arrow versions 0.12.0 to 0.14.1, left memory Array data uninitialized when reading RLE null data from parquet. This affected the C++, Python, Ruby and R implementations. The uninitialized memory could potentially be shared if are transmitted over the wire (for instance with Flight) or persisted in the streaming IPC and file formats.

While investigating UBSAN errors in https://github.com/apache/arrow/pull/5365 it was discovered Apache Arrow versions 0.12.0 to 0.14.1, left memory Array data uninitialized when reading RLE null data from parquet. This affected the C++, Python, Ruby and R implementations. The uninitialized memory could potentially be shared if are transmitted over the wire (for instance with Flight) or persisted in the streaming IPC and file formats.

While investigating UBSAN errors in https://github.com/apache/arrow/pull/5365 it was discovered Apache Arrow versions 0.12.0 to 0.14.1, left memory Array data uninitialized when reading RLE null data from parquet. This affected the C++, Python, Ruby and R implementations. The uninitialized memory could potentially be shared if are transmitted over the wire (for instance with Flight) or persisted in the streaming IPC and file formats.