#vulnerability-management

• AI CVEs in CISA KEV: Ray, LiteLLM, and What's Missing

  Three AI-stack CVEs sit in the CISA KEV catalog: one Ray, two LiteLLM. What confirmed exploitation looks like, and why ShadowRay still is not listed.

• MLflow CVEs: Why 2.14.1 Still Fails Your Scanner

  MLflow carries 80 published advisories and 17 with no patched version. Why scanning 2.14.1 returns unfixable findings, and which ones need real action.

• 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.

• 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.

• PyTorch Security: Notable CVEs and Hardening the Loading Path

  PyTorch's most consequential CVEs cluster on one thing: loading a model file that runs code. A walk through the verified entries and how to harden.

• trust_remote_code and the ML Orchestration CVE Class

  A second family of ML supply-chain CVEs has nothing to do with weights and everything to do with the glue: trust_remote_code and orchestration servers.

• Unsafe Model Deserialization: The Pickle Problem Behind ML CVEs

  Loading a model file can execute arbitrary code. The most repeated vulnerability class in the ML supply chain: the real CVEs, why they recur, and what fixes it.

• ML CVE Database Vulnerabilities: What's Tracked and Missing

  How ML CVE database vulnerabilities are catalogued in NVD and MITRE, why the taxonomy misses AI-specific flaws, and how to build monitoring that covers the gap.

• 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.

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