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AI security incident: CVE-2026-33233 (NVD)

Source disclosure: May 19, 2026 | Published on Radar: October 13, 2026 | Source: NVD | Classification confidence: 45%

Source disclosure is when the advisory appeared in this source (or was added to a catalog), not a confirmed date of exploitation. Radar publication is when this page became available.

This incident is part of the public archive and includes explicit AI-related signals from the cited source material. Review methodology.

AutoGPT is a workflow automation platform for creating, deploying, and managing continuous artificial intelligence agents. In versions 0.6.34 through 0.6.51, the backend deserializes Redis cache bytes using pickle.loads without integrity/authenticity checks. The write path serializes values with pickle.dumps(...) into Redis and the read path blindly invokes pickle.loads(...

Why This Is AI-Related

This page is treated as AI-specific because the source material references artificial intelligence, which places the issue inside an AI workflow, model, assistant, or supporting dependency rather than a generic software bulletin.

  • artificial intelligence

Affected Workflow

Model registries, artifact scanners, notebook workflows, and CI/CD steps that handle model files need immediate review.

Likely Attack Path

The malicious payload is embedded in model artifacts or serialized objects, then executes or bypasses scanning during load and inspection.

Impact

The advisory affects model artifacts or serialized AI assets, which can bypass inspection or execute during load and validation steps. Severity HIGH. Classification confidence 45%. Source channel NVD.

Detection And Triage Signals

  • New or unsigned model artifacts entering the registry
  • Scanner output gaps for pickle or custom model formats
  • Unexpected code paths during model loading or validation jobs

Recommended Response

  • Inventory model artifacts, serialized objects, and scanners that touch the affected package or workflow.
  • Block untrusted model files and revalidate registry, CI, or notebook loading paths before restoring normal operation.
  • Review artifact provenance, scanner output, and recent model-ingestion activity for suspicious changes.

Compliance And Business Impact

Model artifact compromise undermines trust in the training and deployment chain and can create stealthy persistence in ML workflows.

Sources

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