Security readout for executives and security teams
Plain-English summary
Affected Python applications can mistakenly extract files that their archive safety filter rejected. This occurs when tar extraction uses a filter together with TarFile.errorlevel set to 0. If an attacker can supply an archive, blocked content may still be written, creating an integrity risk.
Executive priority
Prioritize remediation where internet-facing or cross-trust workflows automatically extract submitted archives. Other Python systems should be inventoried, then deprioritized if they do not use the affected configuration. Treat this as an integrity-control failure rather than evidence of an active widespread campaign.
Technical view
In affected CPython tarfile implementations, TarFile.errorlevel=0 suppresses the expected rejection outcome during filtered extraction, allowing a filtered member to be extracted instead of skipped. The supplied CVSS 3.1 score is 7.5, reflecting network-accessible, low-complexity, unauthenticated integrity impact without confidentiality or availability impact.
Likely exposure
Exposure is concentrated in services, automation, build systems, or import workflows that process attacker-controlled or otherwise untrusted tar archives while using an extraction filter and TarFile.errorlevel=0. CPython presence alone does not establish exploitability; the vulnerable configuration and an archive input path must both exist.
Exploitation context
The supplied record does not identify known active exploitation, and CVE-2025-4435 is not marked as CISA KEV. Exploitation requires a vulnerable extraction workflow and control over archive content. The sources support unintended file extraction, but do not establish subsequent code execution or broader compromise.
Researcher notes
The key preconditions are filtered tar extraction, errorlevel=0, and influence over archive input. The provided affected-version data does not clearly express complete fixed-version boundaries, so validate each maintained Python branch against the vendor advisory and referenced commits. Do not infer remote code execution solely from the CVSS integrity impact.
Mitigation direction
- Apply a vendor-supported CPython release containing the referenced corrections.
- Until updated, avoid untrusted archives in workflows combining extraction filters with errorlevel=0.
- Restrict archive-processing permissions and isolate its writable destination.
- Review the Python security advisory for branch-specific remediation guidance.
Validation and detection
- Inventory CPython versions used by archive-processing services and automation.
- Search application code for TarFile extraction filters combined with errorlevel=0.
- Trace whether untrusted users or external systems can supply processed tar archives.
- Using harmless test data, confirm rejected members remain absent after extraction.
- Verify deployed runtimes contain the applicable vendor correction.
Public sources used
Generated from the cited source records. This long-tail analysis has not been individually reviewed by a named human.
Conservative CVE-to-ATT&CK context
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ATT&CK lookup starting points
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CWE-682: Exact CWE lookup
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Open ATT&CK lookupCVE-2025-4435 mapping review
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Open ATT&CK lookup- Severity
- High
- CVSS
- 7.5 (3.1)
- Known Exploited
- No
- Published
Vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:N
CNA and ADP enrichment extracted from CVE v5
These fields come from the CVE record and ADP containers, not from Glexia's Take. They preserve time-varying source decisions such as CISA SSVC, KEV status, CVSS metrics, and provider references.
SSVC decision data
CVSS vector scores
1 official scoreWe collect every scored CVSS vector available in the official CNA and ADP containers. When more than one version is present, the table keeps the source vectors side by side instead of collapsing them into the highest score.
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:N3.93.6PSFVulnerability scoring details
Base CVSS 3.1 score
7.5HighVector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:N
Vulnerability timeline
Timeline events are normalized from CVE metadata, CNA source timelines, ADP timelines, and KEV metadata when present.
- CVE reservedCVE Program
The CVE ID was reserved by the assigning CNA.
- CVE publishedCVE Program
The CVE record was published.
- CVE updatedCVE Program
The CVE record metadata indicates this as the latest update time.
ADP provider summaries
Source materials
- CVE List V5 sourceCVE List V5
- https://github.com/python/cpython/issues/135034CVE reference, PSF · issue-tracking
- https://github.com/python/cpython/pull/135037CVE reference, PSF · patch
- https://mail.python.org/archives/list/security-announce@python.org/thread/MAXIJJCUUMCL7ATZNDVEGGHUMQMUUKLG/CVE reference, PSF · vendor-advisory
- https://github.com/python/cpython/commit/3612d8f51741b11f36f8fb0494d79086bac9390aCVE reference, PSF · patch
- https://github.com/python/cpython/commit/9e0ac76d96cf80b49055f6d6b9a6763fb9215c2aCVE reference, PSF · patch
- https://github.com/python/cpython/commit/19de092debb3d7e832e5672cc2f7b788d35951daCVE reference, PSF · patch
- https://github.com/python/cpython/commit/aa9eb5f757ceff461e6e996f12c89e5d9b583b01CVE reference, PSF · patch
- https://github.com/python/cpython/commit/28463dba112af719df1e8b0391c46787ad756dd9CVE reference, PSF · patch
- https://github.com/python/cpython/commit/4633f3f497b1ff70e4a35b6fe2c907cbe2d4cb2eCVE reference, PSF · patch
- https://github.com/python/cpython/commit/9c1110ef6652687d7c55f590f909720eddde965aCVE reference, PSF · patch
- https://github.com/python/cpython/commit/dd8f187d0746da151e0025c51680979ac5b4cfb1CVE reference, PSF · patch
Products and packages named in the record
CWE details
CWE links open Glexia weakness intelligence pages with official CWE context, developer remediation guidance, and related CVE mappings.
Incorrect Calculation
Incorrect Calculation represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.