#ml-cve

• 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.

• Inference Server CVEs: vLLM, Ollama, llama.cpp, Triton

  The serving layer now outproduces model files for critical CVEs. Verified entries in vLLM, Ollama, llama.cpp and Triton, and the three defects behind them.

• 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.

• Hugging Face Model Supply Chain Risk: Pickle Backdoors

  How Hugging Face model supply chain risk works: pickle backdoors, the Transformers RCE CVE cluster, why the Hub scanner misses them, and what cuts risk.

• 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.

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