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https://github.com/paradigmxyz/evmbench

A benchmark and harness for finding and exploiting smart contract bugs
https://github.com/paradigmxyz/evmbench

agents ai audit blockchain blockchain-technology eth ethereum evm security solidity testing ui

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A benchmark and harness for finding and exploiting smart contract bugs

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**evmbench is a benchmark and agent harness for finding and exploiting smart contract bugs.**

How it works | Security | Key services | Repo layout | Quickstart (local dev)

This repository contains a companion interface to the `evmbench` detect evaluation ([code](https://github.com/openai/frontier-evals)).

Upload contract source code, select an agent, and receive a structured vulnerability report rendered in the UI.

## How it works

### Architecture

```
Frontend (Next.js)

├─ POST /v1/jobs/start ───► Backend API (FastAPI, port 1337)
│ ├─► PostgreSQL (job state)
├─ GET /v1/jobs/{id} ├─► Secrets Service (port 8081)
│ └─► RabbitMQ (job queue)
└─ GET /v1/jobs/history │

Instancer (consumer)

┌─────────┴──────────┐
▼ ▼
Docker backend K8s backend (optional)
│ │
└────────┬───────────┘

Worker container
├─► Secrets Service (fetch bundle)
├─► (optional) OAI Proxy (port 8084) ──► OpenAI API
└─► Results Service (port 8083)
```

### End-to-end flow

1. User uploads a zip of contract files via the frontend. The UI sends the archive, selected model key, and (optionally) an OpenAI API key to `/v1/jobs/start`.
2. The backend creates a job record in Postgres, stores a secret bundle in the Secrets Service, and publishes a message to RabbitMQ.
3. The Instancer consumes the job and starts a worker (Docker locally; Kubernetes backend is optional).
4. The worker fetches its bundle from the Secrets Service, unpacks the uploaded zip to `audit/`, then runs Codex in "detect-only" mode:
- prompt: `backend/worker_runner/detect.md` (copied to `$HOME/AGENTS.md` inside the container)
- model map: `backend/worker_runner/model_map.json` (maps UI model keys to Codex model IDs)
- command wrapper: `backend/worker_runner/run_codex_detect.sh`
5. The agent writes `submission/audit.md`. The worker validates that the output contains parseable JSON with `{"vulnerabilities": [...]}` and then uploads it to the Results Service.
6. The frontend polls job status and renders the report with file navigation and annotations.

## Security

`evmbench` runs an LLM-driven agent against uploaded, untrusted code. Treat the worker runtime (filesystem, logs, outputs) as an untrusted environment.

See `SECURITY.md` for the full trust model and operational guidance.

OpenAI credential handling:

- **Direct BYOK (default)**: worker receives a plaintext OpenAI key (`OPENAI_API_KEY` / `CODEX_API_KEY`).
- **Proxy-token mode (optional)**: worker receives an opaque token and routes requests through `oai_proxy` (plaintext key stays outside the worker).

Enabling proxy-token mode:

```bash
cd backend
cp .env.example .env
# set BACKEND_OAI_KEY_MODE=proxy and OAI_PROXY_AES_KEY=...
docker compose --profile proxy up -d --build
```

Operational note: worker runtime is bounded by default; override the max audit runtime with `EVM_BENCH_CODEX_TIMEOUT_SECONDS` (default: 10800 seconds).

## Key services

| Service | Default port | Role |
|---|---:|---|
| `backend` | 1337 | Main API: job submission, status, history, auth |
| `secretsvc` | 8081 | Stores and serves per-job secret bundles (zip + key material) |
| `resultsvc` | 8083 | Receives worker results, validates/parses, persists to DB |
| `oai_proxy` | 8084 | Optional OpenAI proxy for proxy-token mode |
| `instancer` | (n/a) | RabbitMQ consumer that starts worker containers/pods |
| `worker` | (n/a) | Executes the detect-only agent and uploads results |
| Postgres | 5432 | Job state persistence |
| RabbitMQ | 5672 | Job queue |

## Repo layout

```
.
├── README.md
├── SECURITY.md
├── LICENSE
├── frontend/ Next.js UI (upload zip, select model, view results)
├── backend/
│ ├── api/ Main FastAPI API (jobs, auth, integration)
│ ├── instancer/ RabbitMQ consumer; starts workers (Docker/K8s)
│ ├── secretsvc/ Bundle storage service
│ ├── resultsvc/ Results ingestion + persistence
│ ├── oai_proxy/ Optional OpenAI proxy (proxy-token mode)
│ ├── prunner/ Optional cleanup of stale workers
│ ├── worker_runner/ Detect prompt + model map + Codex runner script
│ ├── docker/
│ │ ├── base/ Base image: codex, foundry, slither, node, tools
│ │ ├── backend/ Backend services image
│ │ └── worker/ Worker image + entrypoint
│ └── compose.yml Full stack (DB/MQ + services)
└── deploy/ Optional deployment scripts/examples
```

## Quickstart (local dev)

Ensure Docker and Bun are available.

Build the base and worker images first (required before starting the stack):

```bash
cd backend
docker build -t evmbench/base:latest -f docker/base/Dockerfile .
docker build -t evmbench/worker:latest -f docker/worker/Dockerfile .
```

Start backend stack (API + dependencies):

```bash
cp .env.example .env
# For local dev, the placeholder secrets in .env.example are sufficient.
# For internet-exposed deployments, replace them with strong values.
docker compose up -d --build
```

Start frontend dev server:

```bash
cd frontend
bun install
bun dev
```

Open:

- `http://127.0.0.1:3000` (frontend)
- `http://127.0.0.1:1337/v1/integration/frontend` (backend config endpoint)

## Acknowledgments
Thank you to many folks on the OtterSec team for support, particularly with building the frontend: es3n1n, jktrn, TrixterTheTux, sahuang

[![Apache-2.0 License](https://img.shields.io/badge/license-Apache--2.0-blue.svg)](/LICENSE)