AGENTS.md · git:20260718.a80684f · 2026-07-18 · sha256 4828bdb252e1ef36
AGENTS.md git:20260718.a80684fB
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# AGENTS.md Little Canary is a prompt-injection detection library that uses a sacrificial canary model as an inbound risk sensor. ## Use it for - screening untrusted text before it reaches a main model or agent - combining structural pattern checks with behavioral compromise checks - returning `block`, `flag`, `pass`, or `degraded` state plus advisory text ## Do not use it for - formal security guarantees - audited benchmark comparisons - replacing runtime containment or outbound tool controls ## Minimal commands ```bash pip install -e ".[dev]" little-canary --version little-canary demo --replay # exits 2 until an admitted fixture is packaged little-canary demo --live --backend ollama --model qwen2.5:1.5b --endpoint http://127.0.0.1:11434 little-canary serve --help pytest -q ruff check little_canary tests mypy little_canary ``` ## Output shape - Python API separates routing (`safe`) from coverage (`degraded`, `canary_status`, `analysis_method`, `analysis_status`) - failed/unavailable coverage keeps risk unset and never means an exercised PASS - CLI exposes explicit replay/live demo paths and the local HTTP server - benchmark scripts live under `benchmarks/` and are not part of the default CLI flow ## Success means - when an admitted fixture is packaged, replay reproduces analyzer behavior for those exact bytes without a network call - a build without that fixture must report `REPLAY UNAVAILABLE`; this is a hold, not a safe result - live evidence names its backend, model digest, configuration, and observed result - structural and behavioral layers agree with the documented modes - the Ollama path and OpenAI-compatible protocol adapter pass their declared offline tests; live support remains bound to endpoint-specific evidence ## Common failure cases - the canary backend is unavailable and fail-open routing passes through with a visible degraded state - users expect this tool to replace broader agent runtime controls - benchmark claims are quoted without the methodology caveats in the README ## Maintainer notes - preserve fail-open behavior unless there is an explicit versioned policy change - never treat replay, mock, or static evidence as a current live-model result - keep benchmark caveats aligned with README claims - keep tests offline and mock network calls