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uv allows ZIP payload obfuscation through parsing differentials

Moderate severity GitHub Reviewed Published Oct 29, 2025 in astral-sh/uv • Updated Oct 29, 2025

Package

pip uv (pip)

Affected versions

<= 0.9.5

Patched versions

0.9.6

Description

Impact

In versions 0.9.5 and earlier of uv, ZIP archives were handled in a manner that enabled two parsing differentials against other components of the Python packaging ecosystem:

  1. Central directory entries in a ZIP archive can contain comment fields. However, uv would assume that these fields were not present, since they aren't widely used. Consequently, a ZIP archive could be constructed where uv would interpret the contents of a central directory comment field as ZIP control structures (such as a new central directory entry), rather than skipping over them.
  2. Both local file entries and central directory entries contain filename fields, which are used to place archive members on disk. These fields are arbitrary sequences of bytes, and may therefore be invalid or ambiguous. For example, they may contain ASCII null bytes, in which case different ZIP extractors behave differently: Python's zipfile module truncates the filename at the first null, while uv would skip (not extract) any archive members whose filenames contained nulls. Because of this difference, a ZIP archive could be constructed that would extract differently across different Python package installers.

In both cases, the outcome is that an attacker may be able to produce a ZIP with a consistent digest that expands differently with different Python package installers.

Like with GHSA-8qf3-x8v5-2pj8, the impact of these differentials is limited by a number of factors:

  • To be compromised via this vulnerability, user interaction of some sort is required. In particular, the user must run uv pip install $package or similar with an attacker-controlled $package.
    When using wheel distributions, installation of the malicious package is not sufficient for execution of malicious code, the vicim would need to perform a separate invocation, e.g., python -c "import $package".
  • If a ZIP-based source distribution (which are less common than tarball source distributions), is encountered, malicious code can be executed during package resolution or installation. uv may invoke the malicious code when building the source distribution into a wheel.

Patches

Versions 0.9.6 and newer of uv address both of the parser differentials above, by properly handling comments in central directory entries and by refusing to process ZIPs that contain filename fields that are unlikely to be interpreted consistently across other ZIP parser implementations.

Workarounds

Users are advised to upgrade to 0.9.6 or newer to address this advisory.

Most users should experience no breaking changes as a result of the patch above. However, users who do experience breakage should carefully review their distributions for signs of malicious intent. Users may choose to set UV_INSECURE_NO_ZIP_VALIDATION=1 to revert to the previous behavior.

Attribution

This vulnerability was disclosed by Caleb Brown (Google).

References

@woodruffw woodruffw published to astral-sh/uv Oct 29, 2025
Published to the GitHub Advisory Database Oct 29, 2025
Reviewed Oct 29, 2025
Last updated Oct 29, 2025

Severity

Moderate

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v4 base metrics

Exploitability Metrics
Attack Vector Local
Attack Complexity Low
Attack Requirements None
Privileges Required None
User interaction Passive
Vulnerable System Impact Metrics
Confidentiality None
Integrity High
Availability None
Subsequent System Impact Metrics
Confidentiality None
Integrity None
Availability None

CVSS v4 base metrics

Exploitability Metrics
Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.
Attack Complexity: This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.
Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.
Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the attack.
User interaction: This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable system. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.
Vulnerable System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
Subsequent System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:P/VC:N/VI:H/VA:N/SC:N/SI:N/SA:N

EPSS score

Weaknesses

Improper Input Validation

The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly. Learn more on MITRE.

Interpretation Conflict

Product A handles inputs or steps differently than Product B, which causes A to perform incorrect actions based on its perception of B's state. Learn more on MITRE.

CVE ID

No known CVE

GHSA ID

GHSA-pqhf-p39g-3x64

Source code

Credits

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