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Smart contract security scanner — Slither + ML exploitability prediction

Project description

ChainAudit

Smart contract security scanner powered by Slither + ML exploitability prediction.

PyPI License: MIT


Install

Mac / Linux / Ubuntu

pip install chainaudit
pip install slither-analyzer
pip install solc-select
solc-select install 0.8.24
solc-select use 0.8.24

Windows

Requires Python 3.12. Download from python.org — check "Add Python to PATH" during install. Python 3.13 not yet supported on Windows.

pip install chainaudit
pip install slither-analyzer
pip install solc-select
solc-select install 0.8.24
solc-select use 0.8.24

If on a college/office network, use mobile hotspot for installation.


Usage

chainaudit scan contract.sol               # single file
chainaudit scan ./contracts --recursive    # directory
chainaudit scan contracts.zip              # zip archive
chainaudit scan contract.sol --json        # JSON output
chainaudit scan contract.sol --ml-only     # skip simulation
chainaudit --version                       # show version

Exit code 1 if CRITICAL vulnerabilities found — blocks deployments in CI.


GitHub Action

- uses: aizen299/smart-contract-auditor@v1
  with:
    target: contracts/
    fail-on-critical: true

What It Detects

EVM (Ethereum, Polygon, BNB Chain...)

Severity Examples
CRITICAL Reentrancy, Controlled Delegatecall
HIGH Unchecked Token Transfer, Weak Randomness, tx.origin Auth
MEDIUM Timestamp Dependence, Unchecked Send
LOW Missing Zero Check, Missing Events

L2 / Arbitrum / Optimism — auto-detected

Severity Examples
CRITICAL Cross-Chain Replay Attack, Bridge Reentrancy
HIGH L2 Block Number Assumption, Sequencer Dependence, Address Aliasing
MEDIUM Force-Include Griefing, Gas Price Assumption

L2 rules activate automatically when the scanner detects Arbitrum/Optimism identifiers (ArbSys, xDomainMessageSender etc.) in the contract source.


ML Predictions

Each finding includes an ML-predicted exploitability score trained on the SmartBugs dataset (143 contracts, 88% accuracy).

{
  "title": "Reentrancy",
  "severity": "CRITICAL",
  "ml_exploitability": "CRITICAL",
  "ml_confidence": 0.96
}

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