ML CVE
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AI CVEs in CISA KEV: Langflow, LiteLLM, Ray, and the Gaps
Six AI-stack CVEs sit in CISA KEV: three Langflow, two LiteLLM, one Ray. What confirmed exploitation looks like, and why ShadowRay is not listed.
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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.
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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.
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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.
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PyTorch Security: CVEs and Model Loading Hardening
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.
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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.
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Unsafe Model Deserialization: Pickle CVEs and Defenses
Pickle-based model loading can execute arbitrary code. CVE-2025-32434 shows why safer formats, provenance checks, scanning, and sandboxing matter.
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ML CVE Database: What's Tracked and What's Missing
How NVD and MITRE track ML framework CVEs, why AI-specific risks fall outside the CVE taxonomy, and the monitoring layers that close the gaps.