CVE-2025-61677
Published: 03 October 2025
Summary
CVE-2025-61677 is a low-severity Deserialization of Untrusted Data (CWE-502) vulnerability. Its CVSS base score is 2.5 (Low).
Operationally, exploitation aligns with the MITRE ATT&CK technique Python (T1059.006); ranked at the 32.0th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Data Processing Libraries; in the Supply Chain and Deployment risk domain.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-32652
Vulnerability details
DataChain is a Python-based AI-data warehouse for transforming and analyzing unstructured data. Versions 0.34.1 and below allow for deseriaization of untrusted data because of the way the DataChain library reads serialized objects from environment variables (such as DATACHAIN__METASTORE and DATACHAIN__WAREHOUSE)…
more
in the loader.py module. An attacker with the ability to set these environment variables can trigger code execution when the application loads. This issue is fixed in version 0.34.2.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Data Processing Libraries
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Deserialization of untrusted data from environment variables in Python-based DataChain library enables arbitrary code execution, abusing Python interpreter (T1059.006) and mimicking event-triggered execution via environment variables similar to Python startup hooks (T1546.018).
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.
Penetration testing supplies malicious serialized objects, detecting unsafe deserialization and supporting corrective actions.
Evaluation of untrusted data handling (deserialization testing) reveals unsafe processing, which the required remediation process addresses.
Untrusted serialized data can be deserialized and observed inside the chamber, blocking gadget-chain exploitation outside the sandbox.
Validates or rejects untrusted serialized data before deserialization occurs.
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.
Provenance of associated data allows detection of untrusted sources before deserialization or processing occurs.