CVE-2025-13462
Published: 12 March 2026
Summary
CVE-2025-13462 is a low-severity Improper Input Validation (CWE-20) vulnerability in Python Python. Its CVSS base score is 2.0 (Low).
Operationally, ranked at the 12.7th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-208613
Vulnerability details
The "tarfile" module would still apply normalization of AREGTYPE (\x00) blocks to DIRTYPE, even while processing a multi-block member such as GNUTYPE_LONGNAME or GNUTYPE_LONGLINK. This could result in a crafted tar archive being misinterpreted by the tarfile module compared to…
more
other implementations.
- CWE(s)
Related Threats
No named actor attribution yet. ATT&CK technique mapping in progress for this CVE.
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Security testing and evaluation at multiple SDLC stages directly detects missing or flawed input validation, with the required remediation process ensuring fixes are applied.
Requiring identifiable owners for portable devices reduces the attack surface for unrestricted uploads of dangerous file types via anonymous media.
Security testing and developer training directly verify and enforce proper input validation, reducing exploitability of injection and malformed-data weaknesses.
Dangerous file uploads can be detonated in the chamber to determine malice before any production write or execution occurs.
Prevents unrestricted writing of arbitrary or malicious firmware by keeping hardware write-protect enabled except under tightly controlled manual procedures.
Directly implements checks on information inputs to reject invalid data before processing.
Scans files from external sources on download/open/execute, blocking unrestricted uploads of dangerous file types.
Identifies indicators of injection attacks (command, SQL, LDAP, etc.) via anomaly and attack monitoring.