Best AI Tools for Smart Contract Auditing in 2026

Video transcript

No independent test ranks AI smart contract auditors yet. So build a stack, and verify every finding.

Guardian publishes this guide and builds Helix, an AI auditor. We did not test any tool. Claims come from makers' pages and cited research.

For whole-repository scans, compare V12, Sherlock Audit Engine, Nethermind AuditAgent and Octane, or open-source Hound.

To assist auditors, give Claude Code or Codex the Trail of Bits or Pashov skills. Slither MCP, Aderyn and Olympix add static analysis.

Vendor benchmarks use different datasets, so they do not compare. Nethermind is candid: its tool found 30 percent of what its auditors found.

EVMbench, from OpenAI and Paradigm, tests agents on 120 high-severity bugs. Top scores were 45.6 percent to detect, 41.5 to patch and 72.2 to exploit.

But OpenZeppelin flagged likely training-data contamination, Cointelegraph reported. On 22 newer incidents, BlockSec saw agents detect up to 65 percent, but none produced a working exploit.

While you build, use security skills and static analysis. Add AI review on every pull request and a full scan before the audit. Pair humans and AI, and keep reviewing after launch.

In Guardian's Vanguard audit, Team A uses manual review and fuzzing, while Team B is driven by Helix. Only human-verified findings reach the report.

Read the full guide for every tool's evidence, or get a Vanguard quote at guardian.security.

AI auditing tools range from agents that scan a whole repository to skills that make a coding assistant review like an auditor.

This guide compares each tool on what its maker publishes. It then covers EVMbench, the public benchmark, and its disputes.

Disclosure: Guardian publishes this guide and builds Helix, the AI auditor inside its Vanguard audit. We did not test or benchmark any tool. Every claim comes from the maker’s own pages or the cited research.

How We Chose the Tools

We included tools that review smart contract code and publicly describe how they work. We compared them on five questions:

1. Scope. Whole-repository scans, pull request review, or help for an auditor?
2. Evidence. Published results, with the dataset and how a match was judged.
3. Noise. Does the maker report false positives, not only recall?
4. Verification. Do findings come with a proof of concept or human review?
5. Fit. Languages, integrations, pricing and where your code goes.

We did not rank them. No independent head-to-head exists, and each vendor benchmark uses its own dataset.

The table groups tools by type, in no order of merit.

Comparison at a Glance

Autonomous AI Auditors

These scan a repository on their own and return findings. Treat each as a reviewer whose work still needs checking.

V12

V12 calls itself “an autonomous security system for mission-critical code.” Findings carry a severity, a proof of concept and a fix.

It runs on pull requests or a schedule, or through a CLI, API or MCP server. New accounts get $100 in free credits.

On its own Crypto Security Bench (289 bugs, 31 projects), V12 reports 62.6% recall, against 36.0% for a Claude Code harness on Opus 4.7.

It publishes no measured precision, only an internal estimate of over 35%.

Sherlock Audit Engine

Sherlock calls Audit Engine an orchestration layer. Frontier models, specialist AI auditors and AI-native researchers review the code, and Sherlock validates and deduplicates their findings.

Human review depth depends on the tier. Its product page cites one engagement: 217 valid findings on Polygon Heimdall v2, with 20+ AI auditors.

An October 2026 update added self-serve runs and pull request reviews.

Nethermind AuditAgent

AuditAgent scans Solidity, Cairo and Rust (Solana), and connects to Claude Code and Cursor through MCP. Its Full-Depth Scan is listed at $0.05 per billable line of code, marked as half price.

Nethermind’s case study is candid: across 29 audits, the tool found 30% of what its auditors found.

Nethermind says it “can improve in identifying context-specific vulnerabilities.”

On its own EVMbench run, Nethermind reports 67% recall after validation, which cut flagged findings from 3,230 to 798.

Octane and Almanax

Octane analyzes every pull request, runs on-demand repository scans and can block merges by severity. It covers ten languages, including Solidity and Rust.

We found no definition of its “97.4%” true positive rate.

Almanax runs as a GitHub app with pull request review and threat modeling. It publishes no accuracy data, and its site says it is joining depthfirst.

Hound (open source)

Hound builds knowledge graphs of a codebase. In Bernhard Mueller’s paper, it reached 31.2% recall on five ScaBench projects, against 8.3% for a baseline.

Assistants and Static Analysis

These make a human auditor faster, and most of the open-source options are here.

Claude Code and Codex skills

• Trail of Bits: building-secure-contracts adds vulnerability scanners for six blockchains.
• Pashov Audit Group: solidity-auditor, x-ray for pre-audit threat models, and fizz for fuzz suites.
• Anthropic: claude-code-security-review comments on pull request diffs, with general application security checks.

Anthropic warns its reviewer is not hardened against prompt injection, so run it on trusted pull requests only.

Slither MCP and Aderyn

Trail of Bits’ Slither MCP gives an LLM 12 tools over Slither, including callers, callees and detector results.

Cyfrin’s Aderyn, a Rust-based Solidity analyzer, writes Markdown, JSON and SARIF reports.

Olympix

Olympix combines static analysis, generated tests and mutation testing. Its BugPoCer mixes detectors, symbolic execution, fuzzing and AI, and proves findings with executable proofs of concept.

Its own October 2026 benchmark reports 94 of 160 published audit findings across fifteen protocols, against 53 for Claude Opus 5.5.

What the Benchmarks Show

EVMbench is a public benchmark from OpenAI and Paradigm, with OtterSec. Released February 18, 2026, it uses 120 high-severity bugs from 40 audits, mostly Code4rena contests.

The paper names its limits. Detect cannot credit valid bugs missing from the original report, and false positives go unpenalized.

Patch and Exploit also cover only 45 and 24 of the 120 bugs.

The disputes

• OpenZeppelin found likely training-data contamination and at least four bugs labeled high severity that are not exploitable in practice, Cointelegraph reported.
• BlockSec’s researchers, in a March 2026 paper, retested on 22 incidents from after mid-February 2026. Agents detected up to 65%, but none of 110 agent-incident pairs produced a working exploit.
• Rankings shifted across configurations, tasks and datasets, BlockSec found. The scaffold alone moved results by up to 5 percentage points.

Reading vendor numbers

V12, Nethermind and Olympix each use a different dataset and mostly report recall, so their numbers do not compare.

Ask any vendor: was the dataset public before the model’s training cutoff, who judged a match, and how many findings were false?

How to Combine Them

1. While you build: a coding agent with security skills, plus Slither or Aderyn in CI.
2. On every pull request: an AI reviewer that comments on the diff.
3. Before the audit: a full-repository AI scan, so auditors start from cleaner code.
4. During the audit: humans and AI on the same scope, with invariant fuzzing underneath.
5. After launch: every change reviewed to the same standard.

Nethermind says AuditAgent “doesn’t replace” an audit. In Guardian’s published reports, most Criticals are labeled Logical Error: see business logic vulnerabilities and the critical findings report.

How Guardian Uses AI

Guardian’s flagship audit, Vanguard, puts two independent teams on one scope. Team A uses expert manual review and an exhaustive invariant fuzzing suite.

Team B is driven by Helix, which searches the entire codebase, “spending billions of tokens and several days across hundreds of parallel agents.” Humans verify the context, configuration and depth.

Only human-verified findings reach the report. A $100,000 Defender contest then opens the scope to outside researchers and AI auditors.

See all Guardian audits and pentests.

Frequently Asked Questions

What is the best AI tool for auditing Solidity?

No independent test settles it. Shortlist two or three tools, run them on code with known bugs from your past audits, and compare what they catch and how much noise they produce.

Can an AI tool replace a smart contract audit?

Not on current evidence. Across 29 audits, Nethermind’s tool caught 30% of what its auditors found. Use AI to widen coverage, with humans verifying every finding.

What is EVMbench?

An open benchmark from OpenAI and Paradigm, released in February 2026, that tests AI agents on 120 high-severity bugs from 40 audits. OpenZeppelin and BlockSec have questioned its data and conclusions.

Are there open-source AI auditing tools?

Yes. Hound, Slither MCP, the Trail of Bits and Pashov Audit Group skills, and Anthropic’s claude-code-security-review are open source. Each still needs an AI model or coding agent to run on.

Sources

• OpenAI, Paradigm and OtterSec: EVMbench, Evaluating AI Agents on Smart Contract Security (February 18, 2026)
• Paradigm: Introducing EVMbench (February 18, 2026)
• GitHub: paradigmxyz/evmbench
• Cointelegraph: OpenZeppelin audits OpenAI’s EVMbench, finds data contamination (March 3, 2026)
• arXiv: Re-Evaluating EVMBench, Are AI Agents Ready for Smart Contract Security? (March 11, 2026)
• The Block: BlockSec challenges EVMBench on AI auditing (March 20, 2026)
• V12: Autonomous security system for mission-critical code
• V12: Crypto Security Bench (data of June 4, 2026)
• Sherlock: Audit Engine
• Sherlock: Audit Engine October 2026 update (October 21, 2026)
• Nethermind: AuditAgent
• Nethermind: How Nethermind Security Uses AuditAgent Alongside Manual Audits
• Nethermind: Recall is not enough, false positives and the limits of benchmarking
• Nethermind: How AI detects smart contract vulnerabilities
• Octane: AI-powered application security
• Almanax: The AI Security Engineer
• GitHub: Hound, autonomous agents for code security auditing
• arXiv: Hound, Relation-First Knowledge Graphs for Complex-System Reasoning in Security Audits (September 29, 2025)
• GitHub: Trail of Bits skills
• GitHub: Pashov Audit Group Skills
• GitHub: Anthropic claude-code-security-review
• GitHub: Trail of Bits Slither MCP
• GitHub: Cyfrin Aderyn
• Olympix: Proactive smart contract security
• Guardian: Vanguard