CVE-2025-4905
Published: 19 May 2025
Summary
CVE-2025-4905 is a medium-severity Improper Input Validation (CWE-20) vulnerability in Washington Basestation. Its CVSS base score is 4.8 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploitation for Privilege Escalation (T1068); ranked at the 31.1th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-15711
Vulnerability details
A vulnerability was found in iop-apl-uw basestation3 up to 3.0.4 and classified as problematic. This issue affects the function load_qc_pickl of the file basestation3/QC.py. The manipulation of the argument qc_file leads to deserialization. An attack has to be approached locally.…
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The exploit has been disclosed to the public and may be used. The code maintainer tagged the issue as closed. But there is no new commit nor release in the GitHub repository available so far.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Deserialization vulnerability (CWE-502) in Python's pickle.load() enables local arbitrary code execution via malicious qc_file, facilitating exploitation for privilege escalation (T1068) and abuse of Python scripting interpreter (T1059.006).
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.
Directly implements checks on information inputs to reject invalid data before processing.
Penetration testing supplies malicious serialized objects, detecting unsafe deserialization and supporting corrective actions.
Security testing and developer training directly verify and enforce proper input validation, reducing exploitability of injection and malformed-data weaknesses.
Untrusted serialized data can be deserialized and observed inside the chamber, blocking gadget-chain exploitation outside the sandbox.
Identifies and blocks malicious code introduced through deserialization of untrusted data at system boundaries.
Integrity verification of serialized information can detect tampering before deserialization occurs.
Spam protection mechanisms perform filtering and detection on inbound/outbound messages, directly compensating for missing or weak input validation of unsolicited content.