Update dependency pyarrow to v23 [SECURITY]

[renovate]

This PR contains the following updates:

Package	Change	Age	Confidence
pyarrow	21.0.0 → 23.0.1

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Apache Arrow: Potential use-after-free when reading IPC file with pre-buffering

CVE-2026-25087 / GHSA-rgxp-2hwp-jwgg

More information

Details

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

Severity

• CVSS Score: 7.0 / 10 (High)
• Vector String: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:H

References

• https://nvd.nist.gov/vuln/detail/CVE-2026-25087
• https://github.com/apache/arrow/pull/48925
• https://lists.apache.org/thread/mpm4ld1qony30tchfpjtk5b11tcyvmwh
• http://www.openwall.com/lists/oss-security/2026/02/17/4
• https://github.com/pypa/advisory-database/tree/main/vulns/pyarrow/PYSEC-2026-113.yaml
• https://github.com/advisories/GHSA-rgxp-2hwp-jwgg

This data is provided by the GitHub Advisory Database (CC-BY 4.0).

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Apache Arrow: Potential use-after-free when reading IPC file with pre-buffering

CVE-2026-25087 / GHSA-rgxp-2hwp-jwgg / PYSEC-2026-113

More information

Details

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

Severity

• CVSS Score: 7.0 / 10 (High)
• Vector String: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:H

References

• https://nvd.nist.gov/vuln/detail/CVE-2026-25087
• https://github.com/apache/arrow/pull/48925
• https://github.com/apache/arrow
• https://github.com/pypa/advisory-database/tree/main/vulns/pyarrow/PYSEC-2026-113.yaml
• https://lists.apache.org/thread/mpm4ld1qony30tchfpjtk5b11tcyvmwh
• http://www.openwall.com/lists/oss-security/2026/02/17/4

This data is provided by OSV and the GitHub Advisory Database (CC-BY 4.0).

---

CVE-2026-25087 / GHSA-rgxp-2hwp-jwgg / PYSEC-2026-113

More information

Details

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

Severity

• CVSS Score: 7.0 / 10 (High)
• Vector String: CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:H

References

• http://www.openwall.com/lists/oss-security/2026/02/17/4
• https://lists.apache.org/thread/mpm4ld1qony30tchfpjtk5b11tcyvmwh
• https://github.com/apache/arrow/pull/48925
• https://github.com/advisories/GHSA-rgxp-2hwp-jwgg

This data is provided by OSV and the PyPI Advisory Database (CC-BY 4.0).

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[github-actions]

Polar Signals Profiling Results

Latest Run

Status	Commit	Job	Attempt	Link
🟢 Done	f2b5733		1	Explore Profiling Data

Previous Runs (1)

Status	Commit	Job	Attempt	Link
🟢 Done	0fa318b		1	Explore Profiling Data

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[github-actions]

Benchmarks: Vortex queries 📖

Verdict: No clear signal (low confidence)
Attributed Vortex impact: +2.8%
Engines: DataFusion No clear signal (+4.2%, low confidence) · DuckDB No clear signal (+1.3%, low confidence)
Vortex (geomean): 0.984x ➖
Parquet (geomean): 0.970x ➖
Shifts: Parquet (control) -3.0% · Median polish -0.9%

How to read Verdict and Engines

• Verdict: Overall PR-level signal after subtracting baseline drift estimated from Parquet control rows. It can be Likely improvement, Likely regression, or No clear signal.
• Engines: Per-engine attribution. DataFusion is compared against DataFusion/Parquet controls; DuckDB is compared against DuckDB/Parquet controls. This answers whether each engine improved or regressed independently.
• Confidence: Based on directional consistency, share of rows above the noise floor, and control-run noise.

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datafusion / vortex-file-compressed (0.985x ➖, 0↑ 0↓)

name	PR f2b5733 (ns)	base b7b01d3 (ns)	ratio (PR/base)
vortex_q00/datafusion:vortex-file-compressed	9697956	9831831	0.99
vortex_q01/datafusion:vortex-file-compressed	6108554	6216077	0.98

datafusion / parquet (0.945x ➖, 0↑ 0↓)

name	PR f2b5733 (ns)	base b7b01d3 (ns)	ratio (PR/base)
vortex_q00/datafusion:parquet	20014781	21543706	0.93
vortex_q01/datafusion:parquet	4526831	4713514	0.96

duckdb / vortex-file-compressed (1.010x ➖, 0↑ 0↓)

name	PR f2b5733 (ns)	base b7b01d3 (ns)	ratio (PR/base)
vortex_q00/duckdb:vortex-file-compressed	9830531	9900408	0.99
vortex_q01/duckdb:vortex-file-compressed	6128536	5965326	1.03

duckdb / parquet (0.997x ➖, 0↑ 0↓)

name	PR f2b5733 (ns)	base b7b01d3 (ns)	ratio (PR/base)
vortex_q00/duckdb:parquet	23346598	23497829	0.99
vortex_q01/duckdb:parquet	9407321	9407972	1.00

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No file size changes detected.

[codspeed-hq]

Merging this PR will not alter performance

✅ 1670 untouched benchmarks
⏩ 44 skipped benchmarks¹

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_{Comparing renovate/pypi-pyarrow-vulnerability (f2b5733) with develop (b7b01d3)}

Footnotes

1. 44 benchmarks were skipped, so the baseline results were used instead. If they were deleted from the codebase, click here and archive them to remove them from the performance reports. ↩