falsepass

falsepass

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[CI] [License: Apache-2.0] [PyPI] [Python 3.11+]

Probe RL environment verifiers for false passes (a known-bad completion is rewarded) and false fails (a known-good completion is not).

Website and docs: https://super-eric.github.io/falsepass/ · write-up: Your reward function is an assertion

In one public environment, echoing the question back earned full reward on every task: each question listed all of its candidate answers, and the grader only checked that the right one appeared somewhere in the reply, so a policy could collect full reward by listing everything. falsepass finds this kind of hole before training does.

[falsepass demo: a planted grading defect found from two sides]

An RL environment is a task dataset plus a verifier that turns a model's output into reward. falsepass treats that verifier as the system under test: it calls the environment's own reward code on completions whose correctness is known in advance and reports every disagreement. No model, no rollouts, no GPU.

It works with four frameworks and with plain reward functions, published or still in a local folder:

Install

Needs uv on the PATH (every audit runs in its own throw-away virtualenv) and Python 3.11 or newer.

uvx falsepass demo              # try it, nothing to installuv tool install falsepass       # or keep `falsepass` on the PATHpip install falsepass           # or into an existing environmentuvx --from git+https://github.com/Super-Eric/falsepass falsepass --version   # the unreleased main branch

Quick start

falsepass demo                            # audits a bundled toy environment with one planted defect (about a minute)falsepass init owner/environment          # a Hub environment: package, version and verifiers pin read from the Hubfalsepass init ./my-environment           # or your own, from its folder (pyproject.toml); edits are picked up on every runfalsepass audit my-environment            # probe it; results in audits/my-environment/summary.mdfalsepass results my-environment --json   # headline numbers and failing probes, for scripts and agents

demo writes the toy environment to ./falsepass-demo and audits it. Its grader pays when the reference appears anywhere in the answer, and the summary shows that one line from several sides: the static scan points at it, and the probes that list several answers (P5), retract the answer (P9) or deny it inside the answer slot (P15) are paid. A condensed excerpt of the real output (full file: examples/toy-env/summary.md):

- False-pass ceiling: 1 of 1 (highest reward given to a known-bad completion; via P5 shotgun/all_in_one_slot)- Lazy-policy reward: 0 (best average reward of one fixed strategy that never uses the reference answer; via P3 empty/empty)- False-fail rate: 0.108 (93 known-good completions)
| Probe              | Variant                | Expect | Runs | Failed | Score min / mean / max || P5 shotgun         | all_in_one_slot        | zero   | 10   | 10     | 1 / 1 / 1              || P9 oversized       | correct_then_retracted | zero   | 10   | 10     | 1 / 1 / 1              || P15 hedged_in_slot | denied                 | zero   | 10   | 10     | 1 / 1 / 1              |
Static hint: falsepass_toy_env.py:26 reference_substring_match    return 1.0 if answer in reply else 0.0    confirmed: P9 failed 10/20, P5 failed 10/30

Reward functions (TRL GRPOTrainer style)

falsepass init rewards.py --dataset data.jsonl                       # every function in the file that takes `completions`falsepass init rewards.py --dataset hf:owner/name --split test \    --func accuracy_reward --func format_reward --weight 1 --weight 0.2   # which functions, with the trainer's reward_weightsfalsepass init trl.rewards --framework reward --dataset hf:trl-lib/DeepMath-103K --func accuracy_reward --with math-verifyfalsepass init train.py --dataset call:get_dataset --map to_prompt     # a loader and a row mapper defined in the script itselffalsepass audit rewards

Many graders are not packaged as an environment: a training script defines functions such as accuracy_reward(completions, solution, **kwargs) and hands them to GRPOTrainer with a dataset. falsepass calls each function the way the trainer does, one completion at a time, with every dataset column as a keyword argument (prompts, completions, completion_ids, trainer_state, log_extra, log_metric too), and sums the results with their weights, a None counting as nothing. A completion is a one-message list when the dataset's prompts are conversations or a function reads completion[0]["content"], else text. The reference column is the one a function names (solution), else answer, solution, ground_truth, ... (--answer-column to choose); a worked solution is reduced to its last \boxed{} or what follows #### for the probes, while the functions always receive the column as it is. The dataset is a .jsonl/.json/.csv/.parquet file, hf:owner/name (streamed through datasets), or call:function in the file. The file's third-party imports are installed for the audit (--with adds, --without drops). examples/reward-toy/ has one planted defect.

OpenEnv

falsepass init hf:owner/space                          # a Space on the Hugging Face Hub: cloned next to the auditfalsepass init ./my_env                                # or a local environment folder (openenv.yaml, server/app.py)falsepass init hf:owner/space --answers refs.json      # with reference answers: every probe runsfalsepass audit space

An OpenEnv environment computes its reward in step(), so falsepass drives the environment class the way a session on its server does: reset() to a task, then step() with the reply as the action, in-process, with the Space's own requirements installed and no Docker. (The server's HTTP /reset and /step routes build a fresh environment per request, so they cannot grade a reset followed by a step.) Tasks: audits/<name>/tasks.json lists the keyword sets reset() is called with (most environments pick a task through task_id, task_name, difficulty or a seed; each set may carry an answer), else the Task API when the environment implements it, else repeated reset() calls, one task per distinct prompt; an environment that draws a task at random is reset again until the recorded one comes up. Without a reference answer only the answer-free probes run. A plain reply goes into the action's free-text field; other required fields (a decision enum, a flag) are filled with a constant, and O1 plays every value of such a decision field (a fixed strategy, reported as the lazy-policy reward) while O2 moves a self-reported field (confidence, score) between its minimum and maximum. A reply that is a JSON object naming the action's fields is the action itself: an adapter, or an answers file of such objects, fills several fields that way. examples/openenv-toy/ has one planted defect to try.

Inspect AI and HUD

falsepass init inspect_evals/gsm8k                     # a task from inspect_evals (pinned from PyPI)falsepass init my_eval.py --task hard_split            # a task file; --task picks one of severalfalsepass init ./my-hud-env                            # a HUD environment folder (env.py + tasks.py)falsepass init ./my-hud-env --answers refs.json        # with reference answers: every probe runs

Inspect: the scripted model replaces only the call to generate(); prompt templates, system messages and answer parsing (multiple_choice()) are the task's own code. A solver or scorer that calls a model itself (self-critique, model_graded_qa) is flagged on every row instead of being scored. Tasks that need a sandbox are not supported.

HUD: a task template keeps its expected answer inside the grader, so without --answers only answer-free probes run: an empty reply, the prompt echoed back, and replies that list every integer from 0 to 1000, every letter, yes/no words or the prompt's words (A1; it also runs, with every other probe, when answers are given). A grader that pays those is a substring test in practice. The answers file is JSON: an object from task slug to answer ({"add-2-3": "5"}) or a list in task order (["5", "12"]). To add answers to an existing audit, rerun init with --force. examples/inspect-toy/ and examples/hud-toy/ each have one planted defect to try.

For environment authors: check before you publish

Run falsepass init ./your-env && falsepass audit your-env locally, or add the GitHub Action to the environment's repository so every push is checked:

# .github/workflows/falsepass.ymlon: [push, pull_request]jobs:  falsepass:    runs-on: ubuntu-latest    steps:      - uses: actions/checkout@v5      - uses: Super-Eric/falsepass@main        with:          path: .                         # folder with the environment's pyproject.toml          fail-on: "ceiling>0.1 errors>0" # fail when a known-bad answer earns over 0.1, or scoring raises

The step summary shows the probe table. Gate names: ceiling (highest reward any known-bad completion got), lazy (best average reward of one fixed strategy that never uses the answer), false_fail (share of correct answers refused), errors. Datasets that need a login or an API key at load time cannot be audited in CI without that secret.

For coding agents

• MCP server. falsepass mcp serves the commands as tools (falsepass_scan, _init, _audit, _audit_status, _results, _completion, _reach, _repro, _draft, _upstream, _verify_fix, _discover) over stdio. Audits run in the background, so a client's tool-call timeout never cuts one off; failures come back as tool errors with the reason. Any MCP client starts it with uvx --from "falsepass[mcp]" falsepass mcp; in Claude Code, for example: claude mcp add falsepass -- uvx --from "falsepass[mcp]" falsepass mcp.
• Skill. skills/falsepass/SKILL.md is a ready-made audit procedure in the Agent Skills format: how to run an audit, how to tell a defect from a design choice, and how to write it up for a maintainer. Copy the folder into the agent's skills directory (for Claude Code, ~/.claude/skills/).
• Plain CLI. Every command is non-interactive and writes files; init --json, results --json and scan --json print JSON; results --completion ID prints one completion in full; audit --fail-on ... exits 1 when a condition is met. The output formats are described in docs/OUTPUT.md.

Whatever drives it, the output is a list of leads. An agent should reproduce a lead and read the reward code before calling it a defect, and should not report an environment as broken on the strength of a probe table.

All commands

falsepass init owner/environment          # set up audits/<name>/ for a Hub environment, or pass a local folderfalsepass init hf:owner/space             # an OpenEnv Space on the Hugging Face Hub (cloned into audits/<name>/space)falsepass init rewards.py --dataset d.jsonl   # reward functions written for TRL's GRPOTrainer, with their datasetfalsepass audit environment               # no adapter needed for Q&A tasks: the answer format is inferredfalsepass audit environment --search 120  # add mutation search (scorer calls per direction and example)falsepass audit environment --jobs 1      # score one task at a time (default: up to 8 at once where that is safe)falsepass audit environment --offline     # a cached Hugging Face dataset without the Hub check (also reach, repro --run, verify-fix)falsepass audit environment --fail-on 'ceiling>0.1' --fail-on 'errors>0'   # CI gate, exit 1 when metfalsepass results environment [--json]    # the last run: summary.md, or a compact JSON digestfalsepass results environment --completion ID   # one completion in full (IDs are in the JSON digest)falsepass scan path/to/source [--json]    # static pre-scan only, nothing is installed or runfalsepass discover --days 90 --limit 160  # recently maintained Hub environments and what each needsfalsepass discover --hub hf --days 30      # OpenEnv Spaces on the Hugging Face Hub, most liked firstfalsepass reach environment --probe P15/denied   # how many tasks one failing probe row reaches (every task by default)falsepass reach environment --template '{wrong} or {answer}'   # ...or a completion of your own, filled per taskfalsepass repro environment               # stand-alone script that re-scores the exact failing completionsfalsepass repro environment --run         # ...and run it twice in fresh virtualenvs, compare, store the first runfalsepass rollout environment -n 20       # what a local model's own replies earn (Ollama)falsepass propose environment -n 5        # a local model writes wrong answers and climbs on the rewardfalsepass draft environment               # REPORT.draft.md: candidate findings for manual reviewfalsepass overview -o RESULTS.md          # one table over every audit on diskfalsepass container environment           # container tasks: the key, and mutations of it, written as the output file (Docker)falsepass upstream environment            # find the source repository on GitHub and compare it with the auditfalsepass verify-fix environment --source ../patched/pkg   # rerun the stored repro on a patch: which lines changefalsepass demo                            # the toy environment abovefalsepass mcp                             # MCP server over stdio (`falsepass[mcp]`)

Each audit runs in a throw-away virtualenv pinned by audits/<env>/env.toml, because Hub environments are built for mutually incompatible verifiers versions. Output: audits/<env>/probe-results.json and summary.md. In a clone of this repository, prefix the commands with uv run; uv run pytest runs the kit's own tests.

Checks

Search (--search N) is a seeded beam search over chains of small named mutations. A hit is reported as a minimised recipe, e.g. nothing + pad_think. S2 hits are brittleness candidates and need a human eye: whether "B" in quotes should count as B is a judgement call.

Static pre-scan runs with every audit on audits/<env>/src and on its own with falsepass scan. Narrow AST rules, each derived from a defect found in a real audit; every hint is printed with the verdict of the probe that tests it (confirmed: P6 failed 25/25, not confirmed, not checked).

Adapter inference. Without an adapter.py, the answer format is found by scoring the reference answer in each candidate format (from the parser and the prompt text) and keeping the one the environment's own reward code pays best. Wrong answers come from the label set, a shifted number, or another row. This covers environments with reference answers, single- or multi-turn; tool-use and sandbox environments, and games whose rows carry no answer, need policies: falsepass init --policies writes a skeleton (or --manual for Q&A). A tag that the prompt uses to enclose its input (<conversation>…</conversation>) is not taken for an answer format, and when every format scores the same the plain answer is used. As a control, automatic mode compares the reference with an empty completion and marks the whole result inconclusive when the reward does not separate them.

Multi-turn. Multi-turn environments are scored by running their own rollout loop with a scripted stand-in for the model (V0RolloutScorer), so follow-up prompts, stop conditions and per-episode state are exercised. Search mutates every turn of a script. A script entry can also carry tool calls ({"tool_calls": [{"name": …, "arguments": {…}}]}), which the environment executes with its real tools. Automatic mode also accepts tool-use environments whose tools run in-process: there is no answer format to infer, so it plays the seven T6 policies and T5 and says in the summary that no control was possible. Policies that have to read the task to find a hole are still hand work (falsepass.model.Policy: a function from the conversation so far to the next reply).

v1 tool and multi-turn tasksets (verifiers.v1 with tool servers, its own Env episode loop, or a scripted user) are played as whole episodes through the taskset's own loop, as vf-eval (vf-run from verifiers 0.4.1) runs them: its harness, tool servers, scripted user, stop conditions and rewards (V1EpisodeScorer). The model is a local endpoint on 127.0.0.1 that answers with the policy, so nothing is billed or uploaded. Automatic mode plays T5 and T6 this way; falsepass init <env> --policies writes a v1 skeleton with a solver as the control, and repro replays each recorded episode turn by turn without falsepass. The harness runs in verifiers' subprocess runtime unless env.toml says runtime = "docker". Outside hosts are blocked during episodes (proxies to a closed port; network = ["host"] allows one), so an environment that registers with or posts to a live service does not act there.

Rollout check. falsepass rollout <env> sends a sample of tasks to a model behind an OpenAI-compatible endpoint — a local Ollama server by default, so nothing is billed — and scores the model's own replies with the same scorer the probes use. It flags rows for reading (paid without the reference in the reply; reference in the reply but not paid in full; paid more than the reference; cut off at the token cap) and never calls a flag a finding. For tool-use environments the model is driven through verifiers' own client, so it receives the tool schemas and its tool calls are executed. --endpoint, --model and --tag run a second model next to the first.

Model-written wrong answers. falsepass propose <env> asks the model for a reply that is on the subject and wrong, scores it, and has it rewritten a few times with the grader's score as feedback. For closed answers the reference must not occur in the reply (checked in code); for open-ended ones the wrongness is the model's own opinion, so every row is something to read, not a finding.

Discovery. falsepass discover lists Hub environments updated recently with more than one published version, and reads each wheel to say what it needs (sandbox, tools, LLM judge, API key, reference answers). It needs the prime CLI for the listing. --hub hf lists OpenEnv Spaces on the Hugging Face Hub instead (likes, last update, tags, and the hf:owner/space target for init), from the public listing alone.

Three headline numbers per environment: the false-pass ceiling (highest reward any known-bad completion received), the lazy-policy reward (best average reward of one fixed wrong strategy across examples) and the false-fail rate (share of known-good completions denied full reward). summary.md also shows which reward term paid on known-bad completions.

From a probe run to a report

A probe run produces leads, not findings. To turn one into a finding:

1. falsepass repro <env> writes a script that depends only on verifiers and the environment package and re-scores the exact completions (kept in completions.json). Run it twice from a clean virtualenv; the output must be identical.
2. Read the reward code and find the line that causes it. Decide whether it is a defect or a design choice: a grader that is exploitable on purpose is an observation.
3. falsepass draft <env> writes REPORT.draft.md with each failing probe group as a candidate, the confirmed static hints attached, and the places marked where a person has to judge. Rate what holds with docs/SEVERITY.md.
4. Have someone who did not write the report check every number against the stored output.

From a report to a fix upstream

falsepass upstream <env> searches GitHub for the environment's source (repositories the package names itself in its project URLs or a github.com link in its source or docstrings, then the package name in a pyproject.toml, then its most distinctive class and function names), compares every audited file with the repository's default branch, line endings ignored, and prints what a contributor needs: default branch, licence, last push, open pull requests, how many recent merges came from outside. falsepass verify-fix <env> --source <patched folder> reruns the stored repro with the patched package (or a patched copy of the audit's src/, laid over the release) and prints the lines that changed, storing an unpatched baseline first if there is none; a fix is ready when only the finding's lines move. It reruns the repro script only; falsepass audit <env> --source <patched folder> runs the whole probe suite on the patch.

Adding an environment

falsepass init owner/environment writes audits/<name>/env.toml (package, version, module, index, verifiers pin — all read from the Hub wheel) and unpacks the wheel's source into audits/<name>/src for the static scan. falsepass init ./folder does the same from a local pyproject.toml and records the folder as source, so the folder itself is installed and scanned on every run. An installed Inspect task (inspect_evals/gsm8k) has no wheel to unpack: the audit copies the task's own folder and the modules of the scorers it uses into src/ instead. For Q&A environments that is all. Otherwise add --manual and fill in the generated adapter.py: how to load examples, how to write an answer the way the prompt asks, and what a wrong answer looks like.

Track record

Used on a few dozen public Hub environments in October 2026. Every finding kept was reproduced twice from a clean virtualenv and confirmed by reading the reward code; fixes are being sent to the maintainers as pull requests. Individual environments and counts are not published here until those fixes are in.

Limits

• Leads need a person. The tool finds where a grader pays the wrong thing; deciding whether that is a defect, and how bad, is manual.
• Search is shallow: fixed text mutations, a few hundred scorer calls. It does not train a model against the reward.
• Container tasks have no automatic mode, and environments on hosted sandboxes or with a paid LLM judge are not covered without credentials.
• Supported frameworks: verifiers (v1 tool tasksets: generic tool policies automatically, task-aware ones written as policies), OpenEnv (environments whose step() grades a reply or a structured action; MCP tool environments and hand-written servers without an Environment class need policies or an adapter), Inspect AI (no sandboxed tasks) and HUD (answer-graded templates; tasks graded on what an agent did in a workspace need hand-written policies).

Related tools

• rewardlint — adversarial corpus for single reward functions. falsepass runs it through an environment's reward code when the reference can be substituted, and prints its verdict in summary.md.
• ratctl — static and LLM-assisted scanner of environment source for sandbox exploit patterns.
• VerifierLab — attack campaigns against a wrapped grader under a declared budget, with a ground-truth oracle.
• vlabs-integrity — mutation-kill and isomorphic-relabelling audit for coding rewards with a trusted reference.
• harden-v0 — LLM hacker–fixer loop that hardens Harbor-format agent benchmark verifiers.

Layout

falsepass/           the probe kit (falsepass/mcp_server.py: the MCP tools)tests/               its testsexamples/toy-env/    the demo environment, with its planted defectexamples/openenv-toy/, examples/reward-toy/, examples/inspect-toy/, examples/hud-toy/   the same idea for OpenEnv, reward functions, Inspect AI and HUDskills/falsepass/    agent skill: the audit procedureaction.yml           the GitHub Actiondocs/SEVERITY.md     the rating scale; docs/OUTPUT.md: the output filesaudits/<env>/        created by `falsepass init`; ignored by git

Licence

Apache-2.0, see LICENSE.