Vulnerability Management
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CVE Tracking Tool for ML Stacks: What to Run in 2026
Which CVE tracking tool to run on an ML stack: OSV-Scanner or pip-audit in CI, Dependency-Track for SBOMs, KEV and EPSS to rank fixes.
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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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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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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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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 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.
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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.