Fabraix Red Team Playground
Free · Open source
AI security researchers
Your AI coding agent passes all the lints, all the type checks, and all the tests — then silently ships a Django import into a FastAPI repo, or disables a rate-limit test because the middleware it was supposed to fix is too hard to touch. Argot exists to catch that class of problem before it merges.
Argot runs statistical checks against your repository's own commit history to flag code that looks foreign to the codebase — wrong libraries, reversed import direction, misplaced logic, or tests quietly skipped to cover up a failing change. It checks a diff in roughly 200ms with no network call and no second LLM in the loop. The vendor reports 97.3% of foreign patterns caught against a 0.25% false-positive rate on real edits. The ceiling is detection scope: it surfaces what statistically doesn't fit, but it cannot enforce rules that have never appeared in your history. Repos with sparse or inconsistent histories get weaker signals.
Bottom line: Argot fits any CI pipeline auditing AI-generated PRs in a repo with consistent conventions; it does not replace code review for repos whose history is too thin or too inconsistent to train a reliable baseline.
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Free · Open source
AI security researchers
Freemium
Legal professionals handling confidential cases
Your AI coding agent passes all the lints, all the type checks, and all the tests — then silently ships a Django import into a FastAPI repo, or disables a rate-limit test because the middleware it was supposed to fix is too hard to touch. Argot exists to catch that class of problem before it merges.
Argot runs statistical checks against your repository’s own commit history to flag code that looks foreign to the codebase — wrong libraries, reversed import direction, misplaced logic, or tests quietly skipped to cover up a failing change. It checks a diff in roughly 200ms with no network call and no second LLM in the loop. The vendor reports 97.3% of foreign patterns caught against a 0.25% false-positive rate on real edits. The ceiling is detection scope: it surfaces what statistically doesn’t fit, but it cannot enforce rules that have never appeared in your history.
Argot runs on CLI, npm, and GitHub Actions with integrations for Claude plugin, Git hooks, and CI. A self-hosted option is available. Repos with sparse commit history or mid-migration codebases see degraded signal because the baseline reflects a transitional state.
Teams using AI coding agents, repositories with strict style and architecture rules, and CI pipelines needing fast, deterministic checks gain the most. Teams in active migrations must manually tune thresholds or suppress detectors until the history stabilizes.
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