Apache Arrow: Potential use-after-free when reading IPC file with pre-buffering
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
Apache Arrow R package: Arbitrary code execution when loading a malicious data file
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).
PyArrow, PyArrow: Arbitrary code execution when loading a malicious data file
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).
This vulnerability only affects PyArrow, not other Apache Arrow implementations or bindings.
It is recommended that users of PyArrow upgrade to 14.0.1. Similarly, it is recommended that downstream libraries upgrade their dependency requirements to PyArrow 14.0.1 or later. PyPI packages are already available, and we hope that conda-forge packages will be available soon.
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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.