#ml-security
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ML Model CVE Tracking: How to Monitor Your AI Stack
How to track CVEs across ML frameworks, inference servers, and model artifacts, and what to do when a vendor disputes the advisory anyway.
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Malicious Model File Detection: Auditing ML Models
Pickle serialization flaws, PickleScan bypass CVEs, and a practical detection stack for teams pulling models from public repositories like Hugging Face.
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How to Triage an ML-Stack CVE: A Practical Workflow
A repeatable workflow for taking an ML-library CVE from 'a scanner flagged it' to a defensible decision, without panic-patching or trusting the score.
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Reading an ML Library CVE: What to Extract Beyond the CVSS Score
ML library CVEs are usually scored against a generic threat model that doesn't match how the library is used in production AI systems.