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Source code → LLM proposer → Lean 4 verifier → multi-gate validation.

Lean is the verifier. The LLM is just a proposer.

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A pipeline that takes a function in source code, asks an LLM to translate it into Lean 4 and propose a correctness theorem, then runs five independent gates — sanitizer, Lean compile, axiom allowlist, differential test against the original source, and a second-LLM critic — to decide whether to trust the result.

The point is the gates, not the translation. An LLM can write theorem foo : P := sorry and call it "verified." The whole design exists to make that lie detectable.

A theorem can be formally correct while the implementation it describes still has a security flaw. The repo's seed example is HMAC tag comparison:

• source/token_verify_vulnerable.py — early-exit byte loop; functionally correct, timing-leaky.
• source/token_verify_fixed.py — hmac.compare_digest; constant-time.
• RepoVerify/TokenVerify.lean — both the functional theorem (true for both implementations) and the cost theorem (only true for the fixed one).
• source/attack_demo.py — deterministic comparison-cost oracle that recovers the secret HMAC tag from the vulnerable implementation.

Run the attack:

python source/attack_demo.py

The recovered tag matches the secret. The lesson: a verifier that only proves functional theorems would green-light the vulnerable implementation. Real assurance needs stronger theorems, and you have to ask whether the theorem you proved is the right one.

The generic pipeline (verify/) generalizes this question: for any extracted function, does the proposed Lean theorem actually pin down the function's behavior, or is it a vacuous restatement that any buggy implementation would also satisfy?

Lean 4 / Lake (via elan), plus Python ≥ 3.10:

git clone https://github.com/phunterlau/code2lean.git
cd code2lean
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
lake build

lake build

Type-checks RepoVerify/TokenVerify.lean (the original HMAC demo's formal model: vulnerable + fixed equality, both functional theorems, the leak counterexample, and the constant-time cost theorem).

Set one of the LLM API keys, then run:

export OPENAI_API_KEY=...        # for --provider openai
export GEMINI_API_KEY=...        # for --provider gemini
export ANTHROPIC_API_KEY=...     # for --provider claude

python -m verify.pipeline \
  --example examples/01_insecure_compare \
  --provider gemini \
  --critic openai \
  --max-repair 3

The pipeline:

1. AST-extracts the named function from source.py.
2. Asks the proposer to write a Lean 4 file (function + theorem + proof).
3. Compiles with lake env lean. Repairs on failure.
4. Validates with #print axioms (allowlist: propext, Classical.choice, Quot.sound).
5. Differential-tests Lean #eval outputs against the Python function on every fixture case.
6. Optionally has a second LLM critic verdict the theorem (PASS / WEAK / FAIL).

Exit code:

• 0 — all gates passed
• 1 — sanitizer / Lean / axiom / diff-test failure
• 2 — critic returned WEAK / FAIL / PARSE_ERROR

python -m pytest                              # functional + recovery test
python source/attack_demo.py                  # deterministic timing-leak attack

[source.py] ─► AST extract ─► proposer (LLM) ─► Lean file
                                                     │
                                                     ▼
                                           Gate A: sanitizer
                                           Gate B: lake env lean ──repair loop──► proposer
                                           Gate C: axiom allowlist
                                           Gate D: differential test (Python ↔ Lean)
                                           Gate E: critic (second LLM)
                                                     │
                                                     ▼
                                              PASS / fail

A–D are mechanical gates, no LLM judgment in the verification path. E is the only LLM-judgment step, and it exists to catch vacuous theorems (result ≤ 8 for bit_count8 is true even of a constant-zero implementation; Lean accepts it; the critic catches it). See docs/architecture.md for the why and docs/pipeline.md for the how.

Same 10 hard examples, three proposer/critic pairings:

Lean acceptance (did the proof close?) and theorem strength (did the critic approve?) are independent signals. Gemini closes the most proofs; GPT writes the strongest theorems; Claude is the fastest sweep. Four examples were WEAK across all three proposers — the bottleneck there is the prompt, not the model. Full analysis in docs/benchmarks.md.

code2lean works on any source language with an AST library and a type system that maps to Lean. The current adapter is Python; the architecture is language-agnostic.

Within Python today, the pipeline accepts:

• Single function at module scope (no methods)
• Pure / total / terminating
• Types: bool, int (signed or unsigned), bytes, str, list[T], tuple[A, B]

It does not handle classes, decorators, generators, async, floats, dicts, sets, numpy / external libraries, file or network I/O, exceptions, randomness, or functions that depend on module-level helpers.

Full details and what extending to Rust / TypeScript / Go would look like: docs/scope-and-limits.md.

examples/ has 80 small Python functions (byte/list manipulation, arithmetic, Boolean predicates, crypto-flavored primitives) each paired with a fixture for the differential test. Past run artifacts (last_lean_<provider>.lean, last_proposer_<provider>.txt, last_critic_<critic>.txt, pipeline_*.log) are checked in as samples. See examples/README.md for the full list and --example invocation.

verify/
  pipeline.py                 # orchestrator — gates A–D + repair loop
  extract.py                  # AST extraction → FunctionSpec
  llm.py                      # OpenAI / Gemini / Claude provider abstraction
  critic.py                   # gate E — Phase-1 critic (PASS/WEAK/FAIL)
  gpt_to_lean_security.py     # legacy single-file GPT→Lean demo (the seed)
RepoVerify/
  TokenVerify.lean            # handwritten Lean baseline (the security demo)
  Autogen.lean                # pipeline target — overwritten on each run
  AutogenFromGPT.lean         # legacy target for gpt_to_lean_security.py
source/                       # original HMAC demo Python
tests/                        # pytest for the original demo
examples/                     # 80 functions × fixtures + run artifacts
docs/                         # design notes, walkthrough, benchmarks, roadmap

Can an LLM Formally Verify Your Code? A Feasibility Study — motivation, a full insecure_compare walkthrough, benchmark analysis, and what comes next.

MIT. See LICENSE.