How to Audit a Smart Contract with AI (Free, Step by Step)

Short answer: To audit a smart contract with AI, get the repository into a clean, buildable state, run a fast AI-assisted scan to surface common vulnerability signals, triage each finding against the code, then run a deeper AI audit that reasons about the whole repository and tests specific attack hypotheses. Fix, re-run, and compare. For contracts that will hold meaningful value, finish with an expert human review — AI makes that review faster and cheaper, but does not replace it.

You can try the first step right now: paste a public GitHub repository into Firepan's free smart contract scan. It needs no account or credit card and usually returns results in under five minutes.

Step 1: Prepare the Repository

AI analysis is only as good as the code it can see and build. Before you scan:

Make sure it compiles. A clean forge build or npx hardhat compile from a fresh clone. Missing submodules and unpinned dependencies are the most common reason analysis underperforms.
Separate production code from mocks and tests. Keep test helpers in test/ or mocks/ so they are not confused with deployable contracts.
Write down intent. A short README describing roles, trust assumptions, and what each contract must never allow ("only the timelock can upgrade", "shares are always backed 1:1") gives reviewers — human or AI — something to check behavior against.
Pin the commit. Audit a specific commit hash so results are reproducible and comparable later.

Step 2: Run a Fast AI Scan

Start broad and cheap. Firepan's free Scan runs on any public GitHub repository containing Solidity or Vyper:

Open the free smart contract scan.
Paste the repository URL, for example https://github.com/your-org/your-protocol.
Run the scan and wait for results.

Developers can call the same public endpoint directly:

curl -sS https://api.firepan.com/surface/scan \
  -H 'Content-Type: application/json' \
  -d '{"target":"https://github.com/your-org/your-protocol"}'

The Scan uses deterministic pattern and bug-class detectors with optional AI verification, and returns a risk score, structured findings, quality metrics, and a summary. It is designed for triage: fast, broad, and focused on common, high-signal vulnerability classes such as reentrancy, access control flaws, and unchecked external calls.

For a private repository, start a free trial and connect it through GitHub; Firepan reads only the code needed to run the analysis.

If an AI coding agent is doing the work, it can buy a scan or audit per call with no account through the x402 Agent API.

Step 3: Triage Every Finding

An AI finding is a claim about your code. Treat each one as a hypothesis to confirm:

Read the referenced code path, not just the title. Does the described sequence actually happen?
Check reachability. Is the vulnerable function callable by an attacker, or is it behind a role, a modifier, or an internal-only path?
Check impact. What could an attacker gain — funds, control, a denial of service — and under what conditions?
Write a failing test for anything that looks real. A Foundry test that reproduces the issue is the strongest evidence you can have, and it becomes your regression test after the fix.
Record dispositions. Mark each finding as fixed, accepted with a reason, or a false positive. That history is valuable at audit time.

Our vulnerability database explains the mechanics, testing approach, and mitigations for 30 common classes, which helps when a finding names a class you have not dealt with before.

Step 4: Go Deeper with an AI Audit

A scan looks for known patterns. A deeper AI audit reasons about the repository as a system. In Firepan, the next levels are:

Probe — investigates a repository baseline at a selected commit, so you can compare later results against it.
Audit — a deeper automated adversarial review. HOUND AI builds repository context, generates security hypotheses ("can the first depositor inflate the share price?", "can this oracle read be manipulated in one transaction?"), tests them, curates the findings, and uses Slither as best-effort corroboration where compatible. Findings are tied to files and code paths.

This is where AI finds issues that pattern matching cannot: cross-contract flows, accounting errors, and attack sequences that depend on how components interact. See AI smart contract audit for what an Audit report includes, and plans and pricing for how to run one.

Step 5: Fix, Re-Run, and Compare

After fixing, run the analysis again on the new commit. You are looking for three things: the original findings are gone, the fixes did not introduce new ones, and nothing regressed elsewhere. Connecting the repository lets configured GitHub events trigger repeatable analysis, so later changes can be compared against your baseline instead of starting from scratch each time.

Step 6: Know When You Still Need Humans

AI auditing is strong on breadth and known classes, and increasingly on multi-step reasoning. It remains weaker on:

Economic and game-theoretic design — incentives, MEV exposure, liquidation dynamics.
Business intent — whether the code does what the protocol is supposed to do.
Cross-protocol composability — how your system behaves when an integrated protocol misbehaves.
Governance and trust models — who can change what, and whether that is acceptable.

For anything holding significant value, arrive at an expert audit with AI-cleaned code. You will spend less of the auditors' time on issues a tool could have caught. Firepan also offers human-led Boutique Audits, scoped separately. For a broader view of the category, see our guide to the best AI smart contract audit tools.

Frequently Asked Questions

Q: Can I audit a smart contract with AI for free?

A: Yes, for a first pass. Firepan's free Scan analyzes any public GitHub repository with no account, credit card, or API key, and returns structured findings in minutes. Deeper repository-wide audits and private repositories require a trial, subscription, or pay-per-use access.

Q: How long does an AI smart contract audit take?

A: A free Scan usually returns in under five minutes. Deeper automated audits take longer depending on codebase size, typically from minutes to around two hours. A human audit of a complex protocol often takes weeks.

Q: Can I audit a deployed contract address instead of a repository?

A: Firepan's Scan and Audit work on source repositories. For a quick risk view of a deployed token by address, use Firepan Sentinel, which produces a separate point-in-time token risk scorecard.

Q: Is an AI audit enough before mainnet?

A: For low-value or experimental contracts it may be a reasonable baseline. For protocols that will hold meaningful value, use AI auditing to clean up the code and then commission an expert review focused on design, economics, and integrations.

Q: What languages does AI smart contract auditing support?

A: It depends on the tool. Firepan focuses on EVM contracts written in Solidity and Vyper. Other tools cover Rust for Solana or Cairo for Starknet; check each vendor's documentation.

Q: How do I know whether an AI finding is real?

A: Read the code path it references, check whether an attacker can reach it, and try to reproduce it with a test. Findings that come with a concrete code path and reproducible conditions are far easier to verify than unexplained severity labels.

How to Audit a Smart Contract with AI