bohrin

Everyone tests the AI. Bohrin tests the test.

When you train a model with reinforcement learning, a program decides whether each task was solved. That program — the verifier — is the only thing the model learns from. If it is wrong, the model learns the defect, efficiently and without any visible symptom.

Measured on two widely used code-RL datasets, roughly one task in four accepts a patch that does not fix the bug.

Bohrin finds those tasks in your environments and reports one number.

What it does

This is a real run against scratchpad, a public environment in the verifiers repository — not an illustration:

$ pip install 'bohrin[verifiers]'
$ pip install ./environments/scratchpad      # a taskset is an installed package
$ bohrin audit ./environments/scratchpad --max-tasks 8 --unsafe-local

Bohrin  ·  environments/scratchpad
verifiers_v1 · 8 tasks · 2 probes · isolation: none
  note verifier code ran in-process with no isolation boundary

  determinism    ░░░░░░░░░░░░░░░  no variance observed in 5 runs
  weak_oracle    ███████████████  8 task(s) accept known-wrong solutions

  VERIFICATION GAP: 50 / 100   coverage: 2 of 2 probes

  EXPLOIT ▸ 0: accepted identity_return (reward 1)
           echoes the prompt verbatim instead of answering
           submitted: Call the `scratchpad_roundtrip` tool with word="alpha". It
returns a single word. Then reply wi…
           bohrin audit environments/scratchpad --task 0 --operator
identity_return --unsafe-local

Every finding carries the candidate that passed, why it is wrong, and a command to reproduce it. That last line is copy-pasteable: running it re-runs exactly that one finding, against that one task, with that one operator.

What that finding means

scratchpad grades with self.data.word in answer, and its own prompt contains word="alpha". So a model that echoes the prompt back scores full marks without ever calling the tool — on all 8 tasks.

That matters more than one weak grader. The environment exists to test per-rollout isolation, and its docstring offers a mean reward of 1.0 as the evidence that isolation holds. A mean reward of 1.0 is also what you get from a model that never touches the server, so the number does not support the conclusion drawn from it.

Bohrin reports this as an exploit rather than a lead because echoing the question is structurally not an answer — wrongness established without asking the verifier. Confirmed independently by scoring the same payload through verifiers directly, with Bohrin out of the loop.

Reading a taskset

Bohrin audits verifiers v1 tasksets. A taskset is an installed Python package, so install it before auditing the directory it came from.

Scoring invokes the task's reward functions directly — no agent, no model inference, no rollout — so an audit takes seconds.

A taskset that generates tasks forever is refused unless you bound it with --max-tasks N, rather than run until you notice.

What it will not do without being asked

Scoring runs the taskset's own code. Bohrin refuses to execute it with no isolation boundary unless you pass --unsafe-local, and the level it ran under is recorded in the report. A task whose reward function needs a runtime is refused rather than scored on a partial rubric, because a partial rubric awards full marks to a submission that does nothing.

The two open probes

Weak oracle — will the verifier accept work that is provably incorrect? This is mutation testing with the roles relabelled: your verifier is the test suite, and a surviving mutant is a wrong solution it accepted.

Determinism — does the verifier return the same reward for the same submission? A grader that disagrees with itself injects noise straight into the reward signal.

What they will and will not find

The open probes use six deterministic, model-free operators. They cost nothing but reward invocations and they are reproducible, but they do not find what a motivated attacker finds.

Across six verifiers v1 environments they reported one defect — scratchpad, above — and no false accusations. Of the other five: three measured clean at full coverage, and two Bohrin declined to score, saying so in the report rather than reporting them clean (gsm8k needs a runtime; reverse_text's own reference answer fails its own verifier).

Two of the three "clean" results are a limit of the operators, not a verdict on the grader. glossary accepts any reply containing the answer as a substring, and proposer_solver grades on the last integer in the reply. Both are exploitable; no fixed operator here constructs the payload that does it.

That gap is the honest boundary of the open core, and it is stated here rather than discovered later.

The rule this codebase is built around

Bohrin must never falsely accuse a verifier.

A missed exploit costs you one finding. A false accusation costs the tool its reason to exist. So a candidate is only reported as an exploit when its wrongness is established independently of the verifier being audited — everything else is a lead, not a finding. CI enforces this from both directions, including a clean fixture where any finding fails the build.

Runs on your infrastructure

Bohrin provisions no compute and transmits no environment data. It runs where your environments already run, and it refuses to execute generated candidates unless the required isolation properties are present.

Documentation

What Bohrin is not

• It does not train models and does not provide a reward signal.
• It does not host or execute environments.
• It does not grade model outputs — that is a verifier's job. Bohrin grades the verifier.

Authorisation

Bohrin generates working exploits against verifiers. Audit only environments you own or are authorised to assess — the same norm the security industry applies to offensive tooling.

Not a novelty claim

The techniques are published. Weak oracle is mutation testing; the adversarial hacker/fixer loop is arXiv:2606.08960; the acceptance rates are arXiv:2606.16062. What Bohrin contributes is the productised system and its position — not the algorithms.

License

Apache-2.0. Copyright 2026 Bohrin.