git:20260419.54fabcc to git:20260907.62c0f3c

1 added, 0 removed. Audit A to A.

# AGENTS.md

`hermes-jailbench` is a deterministic jailbreak regression benchmark for LLM endpoints.

## Use it for

- rerunning known jailbreak patterns after a model or prompt change

- producing a structured refusal, partial, and complied report

- smoke-testing the CLI with `--demo` or `--dry-run` before using real credentials

+ - exercising the full client/retry/scorer path offline against `python -m hermes_jailbench.mock_target` (loopback only, no key)

## Do not use it for

- claiming a model is safe against novel attacks

- multi-turn adversarial testing

- semantic judgment of ambiguous responses without human review

## Minimal commands

```bash

pip install -e ".[dev]"

hermes-jailbench --demo

hermes-jailbench --dry-run

pytest -q

ruff check hermes_jailbench tests

```

## Output shape

- terminal summary for demo and standard runs

- markdown or JSON report for saved output

- per-attack verdicts: `REFUSED`, `PARTIAL`, `COMPLIED`, or `ERROR`

## Success means

- attacks run without mutating the library state

- demo and dry-run work without an API key

- scorer output stays deterministic for the same response text

- tests stay offline and green

## Common failure cases

- users expect this tool to generate novel jailbreaks instead of replaying known ones

- API credentials or model IDs are invalid

- a response is ambiguous enough that the deterministic scorer needs manual review

## Maintainer notes

- keep the scorer deterministic and offline

- keep `run_bench()` and CLI behavior aligned with README examples

- do not add live-network behavior to tests