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Mylonite

Model robustness is not the same as application security. A frontier model can resist every generic prompt-injection and still hand an attacker a win, because the hole is in your app's design, not the model's alignment. Mylonite tests whether your app-layer controls are stopping the attack, writes a validated regression test for each weakness, and gates CI so a model upgrade can't silently strip the protection away.

Mylonite is an open-source framework for AI-layer security testing. It targets the AI/agentic part of an application — system prompt, tools, RAG pipeline, agent memory — and emits validated regression tests that gate CI. It deliberately does not test the surrounding traditional code; that work belongs to SAST/DAST tools.

The phased build plan lives in ROADMAP.md.

Status: the full pipeline works

scangeneratevalidategate runs end to end against the bundled reference app and your own MCP (Model Context Protocol) app (--target-file). Findings are proven by the control-efficacy check (the two-build differential on the reference app), emitted as committed pytest regression tests, and surfaced as a gating PR, SARIF, or a JSON bundle. Every claim is checked by an independent verification harness. The surface is deliberately narrow — every shipped feature runs on an MCP app you didn't author and is on a path to third-party proof. See the changelog.

Example: same model, two app versions

The same model, two versions of the bundled app: against the vulnerable version Mylonite catches a send_email dispatched with no approval step (a pure app-design flaw); against the guarded version it finds nothing. The app's design decides — see the full independent scorecard, negatives included.

Where to go next