Multi-model smart-contract security · strength in numbers and thought diversity
AI Consensus Audit runs your code through a group of AI auditors with diverse thinking, then ranks findings by evidence, severity, exploitability, and model convergence. It doesn't simply keep whatever most models agree on — each serious issue has to survive code-specific review before it drives your final report. Fewer blind spots. Fewer false alarms. A clearer verdict before you pay for a manual audit.
Smart contracts · multi-chain codebases · full scans $100, or $50 with $AICA
The problem
AI can read code and flag real vulnerabilities. But one model, however good, still has one reasoning style, one set of assumptions, and one set of blind spots. It may miss a subtle accounting bug, misunderstand protocol logic, over-report a harmless pattern, or fail to connect a warning to a real exploit path. In production, that gap matters — one missed vulnerability can drain funds, freeze assets, or break protocol logic.
Basic multi-model scanners try to solve this by keeping only the findings several models agree on. That helps, but agreement alone is not enough: multiple models can repeat the same false positive — and the most important bug may be found by only one strong reviewer.
Consensus closes the gap when it's evidence-weighted. AI Consensus Audit compares independent findings, weighs the strength of the evidence, challenges disagreements, preserves credible high-impact minority findings, and validates serious issues against the actual code. The result is not majority-vote security — it's a ranked report based on what can be supported, tested, and acted on.
How it works
First, connect your wallet. We use a thirdweb wallet connect, so you know it's one you can trust.
Paste your code or link a repository. AI Consensus Audit ingests it and builds context around contracts, functions, modifiers, external calls, access controls, and critical execution paths.
Several independent AI auditors review your code in parallel — each from a different angle: access control, reentrancy, accounting, oracle risk, economic attacks, upgradeability, state-machine issues, general logic. They don't see each other's findings on the first pass. No groupthink. No single point of failure.
The system merges duplicates, compares root causes, and checks whether auditors point to the same vulnerability — then weighs each issue on code-specific evidence, severity, exploitability, affected functions and assets, model convergence, contradicting evidence, and whether existing protections block it.
When auditors disagree, that's useful signal. The system checks the actual code path, modifiers, require statements, permissions, preconditions, and possible bypasses — then the issue is confirmed, downgraded, marked uncertain, or refuted.
A ranked verdict: a Consensus Risk Score, validated findings, exploitability notes, code-specific explanations, architecture context, and suggested fixes. You see what matters first — and why.
Try it now — free
This is the real engine, live. Drop in a contract or code file and an independent panel of AI auditors will review it, reconcile the findings, challenge weak claims, and return a ranked consensus report — ranked by severity, evidence, confidence, and exploitability, not just by how many models agreed. No signup.
Free live scan for a single file. Full scans are $100, or $50 when paid with $AICA — full repositories, continuous re-scans, PDF reports, and API access are on the paid tier. See Pricing.
What every scan gives you
A single 0–100 risk read, derived from evidence strength, severity, exploitability, disagreement, and model convergence — a weighted signal of how risky the code looks after multi-agent review, not a vote count.
Sorted by severity, confidence, exploitability, evidence quality, consensus strength, and potential impact — so you fix the highest-risk problems first.
Not just what is wrong, but why it matters, how it could be exploited, what assumptions are required, and what an attacker could do with it.
High-impact findings are checked against the actual code path. If exploitable, the report explains the likely attack sequence; if existing protections block it, the finding is downgraded, marked uncertain, or removed.
When auditors disagree, the report explains how the conflict was resolved — confirmed, partially valid, uncertain, or refuted — and why.
An auto-generated map of your contracts, key functions, relationships, external calls, and critical execution paths.
The properties your contract should always preserve, surfaced as a starting point for testing, fuzzing, and deeper review.
Reconnect your repository and every code change can be re-reviewed, so a fix never quietly introduces a new hole.
Why consensus
Put several independent models on the same code and you get broader coverage — but simple agreement is not enough. Several models can agree on the same false alarm. Several can miss the same edge case. One auditor can find a critical issue the others overlooked. That's why AI Consensus Audit uses evidence-weighted consensus. It asks: did the auditors identify the same root cause and point to the same code path? Is the exploit path realistic? Are the preconditions possible? Does the issue affect funds, permissions, pricing, accounting, or protocol state? Did another auditor identify a protection that blocks it? Can the finding survive adversarial review?
Agreement is a signal. Evidence is stronger. The final report is built to surface the vulnerabilities most likely to be real, exploitable, and worth your time.
Every chain. Every language. AI models with diverse thinking.
Most automated scanners are wired to a single virtual machine or ecosystem. AI Consensus Audit is built as an AI audit layer that reasons across smart-contract codebases — Solidity, Vyper, Rust, Move, Cairo, CosmWasm, and other languages. Use it across EVM chains, Solana, Cosmos, Move-based chains, Starknet, and emerging ecosystems. If your protocol spans multiple chains, you shouldn't need a different security workflow for every environment. One panel. One report. One risk view.
Measured, not marketed
AI Consensus Audit is not a replacement for expert human auditors — it's built to make security review faster, sharper, and more focused before a manual audit, before deployment, after major fixes, or during active development. The engine combines independent AI auditors, specialized review perspectives, finding deduplication, evidence-weighted scoring, disagreement review, exploitability validation, and risk-ranked reporting. Human auditors still matter — especially for protocol design, economic assumptions, and complex system behavior — but they shouldn't have to start from zero, and you shouldn't have to pay them to. AI Consensus Audit gives them a cleaner map of where the risk is likely hiding.
Pricing & payment · crypto only
No subscriptions, no seats, no cards. Pay only when you need a review — connect your wallet, pay, and get a ranked consensus report in minutes.
$AICA is the official payment token of AIConsensusAudit.com, sitting at the convergence of crypto, AI, and smart-contract security. Use it to run scans and unlock the preferred rate: $100 standard · $50 with $AICA. Same audit, native-token pricing for builders who scan often — no subscriptions, no credit cards, no sales calls. Connect your wallet, pay with $AICA, and run your scan in minutes.
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For developers
Call the same engine from your CI, your launchpad, your DEX, or your bot — on any chain. Send code, get back ranked findings and a Consensus Risk Score as structured data. Pay per call, no monthly minimum. Ship code that's been through the panel before it ever hits mainnet.
FAQ
$AICA is the official payment token of AIConsensusAudit.com — a platform for evidence-weighted AI smart-contract audits. Pay with $AICA to run full scans at the native-token rate.
The free live scan covers a single file. A full scan is $100 with standard payment, or $50 when paid with $AICA — same engine, same ranked report, half the cost.
No. The scan quality and report are identical. $AICA simply unlocks the preferred payment rate inside the AI Consensus Audit ecosystem.
No — it's the step before one. It clears the obvious and much of the subtle so human auditors spend their time on hard, contextual logic, and so you don't pay audit rates to find a missing access-control check.
Independent AI auditors review your code separately; the system reconciles their findings, weighs the evidence, challenges disagreements, validates serious issues against the code, and ranks what survives. It's evidence-weighted — not a majority vote.
Solidity, Vyper, Rust, Move, Cairo, CosmWasm and more — across EVM chains, Solana, Cosmos, Move-based chains, Starknet, and emerging ecosystems.
Resources & legal
Read the full technical whitepaper, and review the terms and privacy policy that govern use of AIConsensusAudit.com.
Connect your wallet, run your code past the panel, and get a ranked consensus report before it goes live.