PyPI: uv

GHSA-w476-p2h3-79g9

Safety vulnerability ID: SFTY-20251021-47509

### Impact In versions 0.9.4 and earlier of uv, tar archives containing PAX headers with file size overrides were not handled properly. As a result, an attacker could contrive a source distribution (as a tar archive) that would extract differently when installed via uv versus other Python package installers. The underlying parsing differential here originates with astral-tokio-tar, which disclosed this vulnerability as CVE-2025-62518. In practice, the impact of this vulnerability is **low**: only source distributions can be formatted as tar archives, and source distributions execute arbitrary code at build/installation time by definition. Consequently, a parser differential in tar extraction is strictly less powerful than the capabilities already exposed to an attacker who has the ability to control source distributions. However, this particular source of malleability in source distributions is unintentional and not operating by design, and therefore we consider it a vulnerability despite its overlap in capabilities with intended behavior. ### Patches Versions 0.9.5 and newer of uv address the vulnerability above. Users should upgrade to 0.9.5 or newer. ### Workarounds Users are advised to upgrade to version 0.9.5 or newer to address this advisory. Users should experience no breaking changes as a result of the patch above. ### References * See CVE-2025-62518 for the corresponding advisory against astral-tokio-tar

Created at: Jul 10, 2026Updated at: Jul 10, 2026

Overview

uv has differential in tar extraction with PAX headers

Advisory

uv has differential in tar extraction with PAX headers

Affected Package

Affecting uv package, versions<= 0.9.4

Also affects

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How to Fix

Upgradeuvto0.9.5or higher.

Mitigation and Workarounds

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Vulnerable Functions

Functions linked to known vulnerabilities.

Vulnerable function data is available for Enterprise customers

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Safety

Verified by Safety

Our Cybersecurity Intelligence Team reviewed this vulnerability. We combine public data with our own research to find issues not yet reported to public sources.

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