What Is a Hybrid AI and Human Smart Contract Audit?
Quick answer. A hybrid audit puts human auditors and AI tools on the same code, with humans verifying every finding. AI adds breadth on known patterns; humans add protocol context and adversarial reasoning, and independent teams cover each other’s gaps.
Video transcript
AI can now find real smart contract bugs, and exploit them. A hybrid audit pairs that breadth with human teams who verify every finding.
It has three parts. Independent human teams review the same scope. AI tools search the whole codebase for candidates. Human verification decides what reaches the report.
AI brings breadth on well-known patterns but misses context-specific bugs. Humans bring protocol knowledge and adversarial reasoning.
Published research shows AI coverage is uneven. The best EVMbench score was 45.6 percent of known high-severity bugs.
Humans miss bugs too. In Code4rena's NOYA contest, 173 wardens took part, yet some High-severity bugs were found by just one.
So a second independent team is not redundant. It is another chance at what the first one missed.
Guardian, which publishes this article, uses this in Vanguard. Two independent teams take one scope, one of them powered by Helix, Guardian's AI auditor. They cross-check each other, and only human-verified findings make the final result.
In Guardian's words, AI searches exhaustively and humans verify context, configuration and depth. Guardian has not published a Helix benchmark.
Choose a hybrid audit when code will hold significant value, is new or complex, or when one team's blind spot would be costly.
Read the full article for questions to ask any provider, or get a Vanguard quote at guardian.security.
AI models can now find real smart contract bugs, and exploit them.
In Anthropic’s December 2025 simulations, agents wrote working exploits for 19 contracts hacked after the models’ knowledge cutoffs. They also found two new zero-days among 2,849 recently deployed contracts.
Defenders can use the same agents. The question is how to pair them with human auditors.
The Model in Brief
A hybrid AI and human audit has three parts:
- Independent human teams review the same scope separately.
- AI tools search the whole codebase for candidate bugs.
- Human verification decides which candidates reach the report.
The last part separates it from an AI scan, where you verify the output yourself. Our explainer on AI smart contract audits covers that difference.
Each side covers the other’s gaps:
| AI tools | Human auditors | |
|---|---|---|
| Strong at | Well-known patterns, whole-codebase breadth | Protocol-specific knowledge, adversarial reasoning |
| Weak at | Context-specific bugs; noise that needs filtering | Bugs another reviewer would have caught |
What AI Catches and Misses
Published evaluations agree on the shape of the result: AI is broad, but its coverage is uneven.
| Study | Result |
|---|---|
| EVMbench, OpenAI and Paradigm (Feb 2026) | Best detect score: 45.6% of 120 known high-severity bugs |
| EVMbench re-evaluation (Mar 2026) | Up to 65% detected on 22 newer incidents; no end-to-end exploit |
| Nethermind AuditAgent (Sep 2025) | Found 30% of auditor findings across 29 audits |
| GPTScan, ICSE 2024 | Found 9 vulnerabilities that human auditors missed |
The authors draw the same lessons:
- Breadth: agents “reliably catch well-known patterns,” the re-evaluation found.
- Coverage: a model can score well while “still missing critical vulnerabilities in the same codebase.”
- Role: Nethermind calls its tool “most effective as a pair auditor rather than a standalone solution.”
For each tool’s claims and the disputes over EVMbench, see the best AI tools for smart contract auditing.
What Humans Catch and Miss
Humans supply what the tools lack. The re-evaluation’s authors suggest a split where “AI handles breadth and human auditors contribute protocol-specific knowledge and adversarial reasoning.”
Their verdict: agents “cannot replace human judgment.”
Humans miss bugs too.
Different reviewers, different bugs
Audit contests show how unevenly bugs are found. In Code4rena’s NOYA contest, from April 26 to May 17, 2024, 173 wardens contributed reports.
They found 23 unique High-severity bugs. Several were reported by a single warden, among them H-05 and H-09.
EVMbench’s authors saw the same pattern in Code4rena data. For many bugs, the number of auditors who found them was “in the single digits.”
So a second independent team is not redundant. It is a second chance at the bugs the first one missed.
For how contests compare with private audits, see audit contest vs private audit.
How Vanguard Applies It
Guardian’s Vanguard audit uses this structure. Its page describes “two independent teams, one scope.”
| Part | As the site describes it |
|---|---|
| Team A | Expert manual review; gets the most out of the fuzzing suite |
| Team B | Directs billions of tokens throughout every corner of the codebase |
| Cross-check | Competitive incentives; each team cross-checks the other |
| Fuzzing | Core safety properties written as executable invariants |
| Verification | Only human-verified findings become the final result |
| After the audit | A $100,000 Defender contest and a hosted bug bounty |
Helix, Guardian’s AI auditor, powers one of the two teams. The site says it searches the entire codebase, “spending billions of tokens and several days across hundreds of parallel agents.”
The Vanguard announcement sums up the split: “AI performs an exhaustive search for every possible bug and exploit, humans verify the context, configuration, and depth.”
The announcement adds that Helix has earned “confirmed bug bounties in many of the most respected protocols in DeFi.” Guardian has not published a Helix benchmark.
When to Choose It
A hybrid audit fits code where one missed bug is expensive. Choose one when:
- The code will hold significant value at launch.
- It is new, complex or heavily customized.
- One team’s blind spot would be costly.
- You want AI breadth without unverified AI output.
Developers can also run agent scans as “a pre-deployment check,” the re-evaluation’s authors suggest.
Questions to ask any provider
- Are the teams independent, and do they cross-check each other?
- Does a named human verify every AI finding?
- Which commit and files did each team cover?
- What has the provider published about its AI tool’s accuracy?
- Who reviews the fixes, and later changes?
When the report arrives, see how to read a smart contract audit report.
Frequently Asked Questions
What is a hybrid AI and human smart contract audit?
An audit in which human auditors and AI tools review the same code, and humans verify every AI finding before it reaches the report. Guardian’s Vanguard also uses two independent teams that cross-check each other.
Can AI replace human smart contract auditors?
Not on published evidence. Across 29 audits, Nethermind’s tool found 30% of what its auditors found, and researchers re-testing EVMbench concluded agents “cannot replace human judgment.” AI adds breadth; humans add context and adversarial reasoning.
Why use two independent audit teams?
Different reviewers find different bugs. In Code4rena’s NOYA contest, 173 wardens took part, yet some High-severity bugs were reported by a single warden. A second independent team is another chance at what the first one missed.
When is a hybrid audit worth it?
When the code will hold significant value, is new or complex, or when one team’s blind spot would be costly. Researchers also suggest developers run AI scans as a pre-deployment check.
Sources
- OpenAI, Paradigm and OtterSec: EVMbench, Evaluating AI Agents on Smart Contract Security (February 18, 2026)
- Paradigm: Introducing EVMbench (February 18, 2026)
- arXiv: Peng, Wu and Zhou, Re-Evaluating EVMBench, Are AI Agents Ready for Smart Contract Security? (March 11, 2026)
- Anthropic: AI agents find $4.6M in blockchain smart contract exploits (December 1, 2025)
- Nethermind: How Nethermind Security Uses AuditAgent Alongside Manual Audits (updated September 20, 2025)
- arXiv: Sun et al., GPTScan, Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysis (ICSE 2024)
- Code4rena: NOYA audit report (contest April 26 to May 17, 2024)
- Guardian: Vanguard
- Guardian: Guardian Announces Vanguard
Put two teams and Helix on your code
Vanguard pairs expert manual review and invariant fuzzing with Helix, and only human-verified findings reach the report.