AI-Powered Smart Contract Security Audit: EVMbench Methodology

#ethereum #blockchain #security #web3

AI-Powered Smart Contract Security Audit: EVMbench Methodology

A practical guide to using AI for EVM smart contract security auditing — the methodology we use in production at ODEI.

The EVMbench Approach

EVMbench is a systematic methodology for AI-assisted smart contract auditing. Unlike simple pattern matching, it:

Decompiles and normalizes the bytecode
Maps control flow and data dependencies
Checks against 20+ vulnerability patterns
Generates PoC code for each finding
Provides severity-rated remediation steps

Vulnerability Classes Covered

Critical (auto-REJECT):

Reentrancy (classic and cross-function)
Integer overflow/underflow
Unchecked external calls

High:

Access control failures
Flash loan attack surfaces
Oracle manipulation vectors

Medium:

Missing events for state changes
Gas griefing vectors
Improper initialization

Low:

Unused variables and functions
Redundant code paths

Practical Example

// Vulnerable contract
contract VulnerableEscrow {
    mapping(address => uint256) public balances;

    function withdraw() public {
        uint256 amount = balances[msg.sender];
        // REENTRANCY: external call before state update
        (bool sent,) = msg.sender.call{value: amount}("");
        require(sent, "Failed");
        balances[msg.sender] = 0;  // Too late!
    }
}

ODEI's audit would flag: [CRITICAL] Reentrancy in withdraw() at line 7

With remediation:

function withdraw() public {
    uint256 amount = balances[msg.sender];
    balances[msg.sender] = 0;  // State update first (CEI pattern)
    (bool sent,) = msg.sender.call{value: amount}("");
    require(sent, "Failed");
}

Using ODEI's Audit Service

import requests

response = requests.post(
    "https://api.odei.ai/api/v2/guardrail/check",
    headers={"Authorization": "Bearer TOKEN"},
    json={
        "action": "deploy smart contract",
        "context": {
            "contract_code": your_solidity_code,
            "chain": "base"
        },
        "severity": "critical"
    }
).json()

findings = response.get("findings", [])
for f in findings:
    print(f"[{f['severity']}] {f['description']}")