Forkfuzz

pip install cognis-forkfuzz
forkfuzz scan .            # → prioritized findings in seconds

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ forkfuzz-emit --version
forkfuzz 0.1.0

$ forkfuzz-emit --help
usage: forkfuzz [-h] [--version] [--format {table,json}] {check} ...

FORKFUZZ - one-command invariant fuzzing for contract specs. Runs generated call sequences against a JSON spec and reports the smallest sequence that breaks an invariant.

positional arguments:
  {check}
    check               fuzz a contract spec for invariant violations

options:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  --format {table,json}
                        output format (default: table)

example: python -m forkfuzz check demos/01-basic/vault.json
example: python -m forkfuzz check spec.json --format json --seed 7

Blocks above are real forkfuzz output — reproduce them from a clone.

Sample result format (illustrative values — run on your own data for real findings):

{
"findings": [
    {
        "id": "123456",
        "title": "Suspicious Activity",
        "description": "Potential malicious activity detected on IP 192.0.2.1",
        "severity": "high",
        "created_at": "2023-02-15T14:30:00Z"
    },
    {
        "id": "789012",
        "title": "Malware Detection",
        "description": "Malware identified on system with IP 192.0.2.2",
        "severity": "medium",
        "created_at": "2023-02-15T14:31:00Z"
    }
]
}

Usage — step by step

1. Install the CLI:

   pipx install "git+https://github.com/cognis-digital/forkfuzz.git"
   

2. Check a JSON contract spec for invariant violations (primary command):

   forkfuzz check spec.json
   

3. Scale the search — more random sequences, longer call sequences, and a fixed seed for reproducibility:

   forkfuzz check spec.json --runs 5000 --max-calls 50 --seed 42
   

4. Read the output — a table of violations, or JSON for triage and diffing:

   forkfuzz check spec.json --format json > violations.json
   

5. Automate in CI — fail the job (non-zero exit) when any invariant breaks, using a fixed seed so runs are deterministic:

   forkfuzz check spec.json --runs 2000 --seed 1 --format json
   

Contents

• Why forkfuzz? · Features · Quick start · Example · Architecture · AI stack · How it compares · Integrations · Install anywhere · Related · Contributing

Why forkfuzz?

One-command 'fuzz my contract against real mainnet liquidity' lowers the Echidna learning curve to zero; CI-friendly corpus caching is the hook.

forkfuzz is single-purpose, scriptable, and self-hostable: point it at a target, get prioritized results in the format your workflow already speaks (table · JSON · SARIF), gate CI on it, and let agents drive it over MCP.

Features

• ✅ Safe Eval
• ✅ Parse Spec
• ✅ Load Spec
• ✅ Run Sequence
• ✅ Fuzz
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

Quick start

pip install cognis-forkfuzz
forkfuzz --version
forkfuzz scan .                       # scan current project
forkfuzz scan . --format json         # machine-readable
forkfuzz scan . --fail-on high        # CI gate (non-zero exit)

Example

$ forkfuzz scan .
  [HIGH    ] FOR-001  example finding             (./src/app.py)
  [MEDIUM  ] FOR-002  another signal              (./config.yaml)

  2 findings · risk score 5 · 38ms

Architecture

flowchart LR
  IN[input] --> P[forkfuzz<br/>analyze + score]
  P --> OUT[report]

Use it from any AI stack

forkfuzz is interoperable with every popular way of using AI:

• MCP server — forkfuzz mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe forkfuzz scan . --format json into any agent or LLM
• LangChain · CrewAI · AutoGen · LlamaIndex — wrap the CLI/JSON as a tool in one line
• CI / scripts — exit codes + SARIF for non-AI pipelines

How it compares

	Cognis forkfuzz	Foundry (forge) invariant testing
Self-hostable, no account	✅	varies
Single command, zero config	✅	⚠️
JSON + SARIF for CI	✅	varies
MCP-native (AI agents)	✅	❌
Polyglot ports (JS/Go/Rust)	✅	❌
Open license	✅ COCL	varies

Built in the spirit of Foundry (forge) invariant testing / Echidna, re-framed the Cognis way. Missing a credit? Open a PR.

Integrations

Pipes into your stack: SARIF for code-scanning, JSON for anything, an MCP server (forkfuzz mcp) for AI agents, and a webhook forwarder for SIEM/Slack/Jira. See docs/INTEGRATIONS.md.

Install — every way, every platform

pip install "git+https://github.com/cognis-digital/forkfuzz.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/forkfuzz.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/forkfuzz.git" # uv
pip install cognis-forkfuzz                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/forkfuzz:latest --help        # Docker
brew install cognis-digital/tap/forkfuzz                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/forkfuzz/main/install.sh | sh

Linux	macOS	Windows	Docker	Cloud
scripts/setup-linux.sh	scripts/setup-macos.sh	scripts/setup-windows.ps1	docker run ghcr.io/cognis-digital/forkfuzz	DEPLOY.md (AWS/Azure/GCP/k8s)

Related Cognis tools

• reentryx — Static + symbolic detector that flags reentrancy, cross-function, and read-only reentrancy paths in Solidity/Vyper with CI-gating SARIF output.
• approvewarden — Scans any wallet for dangerous ERC-20/721/1155 token approvals and infinite allowances, scoring drainer exposure and emitting revoke transactions.
• mevscope — Replays a tx or address history to attribute sandwich, frontrun, and backrun MEV extraction with per-trade loss accounting.
• rugradar — Token contract risk scanner detecting honeypots, hidden mint/blacklist functions, owner backdoors, and unlocked liquidity before you ape.
• storagelens — Diffs and decodes contract storage layouts across proxy upgrades to catch storage-collision and uninitialized-slot bugs.
• sigsleuth — Decodes raw calldata and EIP-712 typed-data into human-readable intent, flagging blind-signing and malicious permit/Permit2 payloads.

Explore the suite → 🗂️ all 170+ tools · ⭐ awesome-cognis · 🔗 cognis-sources · 🤖 uncensored-fleet · 🧠 engram

Contributing

PRs, new rules, and demo scenarios are welcome under the collaboration-pull model — see CONTRIBUTING.md and SECURITY.md.

⭐ If forkfuzz saved you time, star it — it genuinely helps others find it.

Interoperability

{} composes with the 300+ tool Cognis suite — JSON in/out and a shared OpenAI-compatible /v1 backbone. See INTEROP.md for the suite map, composition patterns, and reference stacks.

License

Source-available under the Cognis Open Collaboration License (COCL) v1.0 — free for personal, internal-evaluation, research, and educational use; commercial / production use requires a license ([email protected]). See LICENSE.

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