CVE-2025-5173
Published: 26 May 2025
Summary
CVE-2025-5173 is a medium-severity Improper Input Validation (CWE-20) vulnerability in Humansignal Label Studio Ml Backend. Its CVSS base score is 4.8 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Python (T1059.006); ranked at the 27.7th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Computer Vision; in the Supply Chain and Deployment risk domain.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-16264
Vulnerability details
A vulnerability has been found in HumanSignal label-studio-ml-backend up to 9fb7f4aa186612806af2becfb621f6ed8d9fdbaf and classified as problematic. Affected by this vulnerability is the function load of the file label-studio-ml-backend/label_studio_ml/examples/yolo/utils/neural_nets.py of the component PT File Handler. The manipulation of the argument path leads…
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to deserialization. An attack has to be approached locally. This product takes the approach of rolling releases to provide continious delivery. Therefore, version details for affected and updated releases are not available.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Computer Vision
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ml, yolo
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Deserialization vulnerability (CWE-502) in torch.load allows loading malicious pickle data for arbitrary Python code execution (T1059.006). As a local attack in a backend service context, it facilitates privilege escalation via exploitation (T1068).
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.