ML Supply Chain
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ML Pipeline Supply Chain Attack: From Poison to Production
An ML pipeline supply chain attack turns trusted models, datasets, dependencies, or build artifacts into code execution or persistent model backdoors.
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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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Model File Format Security: Pickle, Safetensors, GGUF
Which model formats can execute code when loaded, which only crash, and which are inert. A format-by-format comparison built from the verified CVE record.
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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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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 Library CVE Analysis 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.