Agentic Misalignment Research Framework

Agentic Misalignment Research Framework is included in the Awesome MLSecOps AI Agent and MCP Security directory. The community-maintained README describes it as: “MIT-licensed harness behind Anthropic's agentic misalignment study that generates fictional corporate scenarios with blackmail, leaking, and replacement-threat conditions, runs them across 40+ models, and classifies whether the agent carried out the harmful insider action.” Its MLSecOps relevance is the security of agent identities, tool permissions, memory, credentials, approval paths, sandboxes, runtime actions, or MCP integrations. The linked first-party source is the anthropic-experimental/agentic-misalignment repository on GitHub. A technical review should test the project's documented evidence across four criteria: Tool-level authorization, Memory and context controls, Sandbox boundaries, and Audit and approval workflows. Compare that evidence with the intended architecture and threat model. Catalog inclusion establishes relevance to this security category; it is not a certification, comparative ranking, or endorsement. Confirm current capabilities, maintenance, licensing, limitations, and deployment assumptions in the first-party documentation before adoption.

MIT-licensed harness behind Anthropic's agentic misalignment study that generates fictional corporate scenarios with blackmail, leaking, and replacement-threat conditions, runs them across 40+ models, and classifies whether the agent carried out the harmful insider action

Neutral catalog description synchronized from the Awesome MLSecOps README