The share of reported findings that are not real, exploitable bugs. Benchmarks that count only recall against a curated bug list cannot measure it.
AI Auditing Tools for Cryptography & ZK
AI bug-finding and auditing tools for cryptographic code, zero-knowledge circuits and smart contracts: what each one actually finds, what it costs, and which firms stand behind the results.
The index
29 tools across 5 categories, in index order. Full comparison with approach and maintainer on the tools page.
Tools by category
Grouped by what each tool targets. Bar length is the number of tools listed in the category; each row opens the category guide.
Firms that run AI-assisted audits
zkSecurityListed firstCryptography and ZK audits with AI tooling built in-house: zkao, zk-skills, zkbugs; human validation of every finding
zkSecurity is a cryptography security firm with more than a hundred audits across zero-knowledge, MPC, FHE, post-quantum and consensus systems. It builds zkao, the continuous AI scanner for cryptography code, publishes zk-skills and the zkbugs benchmark as open source, and offers enterprise packages that combine zkao with its security engineers and manual audits. Its 2026 case studies (Cloudflare CIRCL, OpenVM, Bron Labs) are the most detailed public accounts of AI findings in cryptographic code, including what the AI got wrong.
Listed first because it is the only firm on this index whose AI tooling was built for cryptographic and ZK code, with upstream-confirmed critical results (seven CIRCL bugs, OpenVM CVE-2026-46669, four bron-crypto zero-days), an open benchmark and open skills, and explicit human-in-the-loop validation by cryptographers.
Trail of BitsAI-native security practice: Buttercup, 201 open-source skills, about 20 percent of reported bugs first surfaced by AI, all human-validated
Trail of Bits built Buttercup, the AIxCC runner-up, and has reorganised its audit practice around AI: it reports 15 to 200 AI-surfaced candidate bugs per week on suitable engagements, about 20 percent of reported findings first surfaced by AI, every one validated by an auditor, and publishes 201 skills and 94 plugins as open source. It has a cryptography and ZK practice.
ZellicV12 autonomous Solidity auditor alongside human audits; ZK and Rust work
Zellic builds the V12 autonomous Solidity auditor and continues human audits across EVM, Rust and ZK. It owns Code4rena, which announced it is closing.
Nethermind SecurityAuditAgent as a second layer after manual audits; published recall data
Nethermind Security runs AuditAgent after every manual audit as a second layer and publishes its recall against its own human findings (30 percent average, 42 percent of criticals). It also has a formal verification team working in Lean and EasyCrypt.
SherlockSherlock AI plus audit contests and private audits
Sherlock combines its Sherlock AI product with contest-based and private human audits, and has published a controlled precision study of the AI.
Cantina (Spearbit)AI-native AppSec platform with an enterprise AI code analyzer and a 9,000-researcher network
Cantina, from Spearbit, markets an AI-native application security platform with an enterprise AI code analyzer, combined with human expert review from a network of more than nine thousand researchers. It is unrelated to the agentic SecOps startup of the same name launched in July 2026.
Consensys DiligenceAgentic vulnerability mining as a co-audit workflow guided by veteran auditors
Consensys Diligence describes an agentic vulnerability-mining workflow in which swarms of parallel agents act as lead generators and a confirmation layer, guided by veteran auditors, alongside its symbolic-execution tooling.
CyfrinAderyn static analyzer, Solodit API for AI agents, CodeHawks contests; an AI formal verification engagement for Lido
Cyfrin maintains the Aderyn static analyzer (not AI), opened its Solodit database of more than fifty thousand audit findings to AI agents via an API, runs CodeHawks contests, and lists an AI formal verification engagement for Lido's Circuit Breaker (April 2026) in its public reports.
OpenZeppelinAI Auditor within Program Security; audited EVMbench
OpenZeppelin markets an AI Auditor within its Program Security offering and published the March 2026 audit of EVMbench that identified invalid high-severity items and contamination risk. Product details are not public.
QuillAuditsQuillShield AI plus human audits across 1,400 projects
QuillAudits pairs its QuillShield AI auditor and open-source Claude skills with human audits, reporting more than 1,400 projects audited.
CertoraFormal verification core; AI Composer for prover-checked code generation
Certora's core is the open-sourced Certora Prover; its AI work (AI Composer, Concordance) uses the prover to check model output rather than to scan code. Human audits continue.
VeridiseFormal methods and static analysis for ZK (Picus, ZK Vanguard, LLZK); no public LLM tooling
Veridise is a strong ZK audit firm (RISC Zero, Linea, Succinct, Semaphore) whose tooling is formal and static (Picus, Vanguard, ZK Vanguard, OrCa, LLZK) rather than LLM-based. It is listed for teams weighing AI scanners against solver-based alternatives for circuits.

Recent developments
- 2026-09-07zkSecurity: the year finding and exploiting bugs became cheapArgues for layered continuous security; counts crypto hacks rising from 16 in January to 50 in August 2026.
- 2026-08-22AI Grinding for cryptanalysis paperAgent-generated hypotheses tested by exact computation; claims reproducible failures in eight published constructions.
- 2026-08-21Claude Security available to enterprises on Claude Mythos 5Billed as standard token usage.
- 2026-08-20Ethereum Foundation, Yukon and zkSecurity launch better.codesAI agents raise a Lean-checked soundness bound; the kernel judges every submission.
- 2026-08-15AISLE reports six curl CVEs after Mythos and Codex Security found noneLow-severity issues fixed in curl 8.22.0.
- 2026-08-05zkSecurity releases zk-skills and circom-auditor66 of 70 on zkbugs direct mode; 40 of 56 on full codebases.
Vocabulary
Precision is valid findings divided by all findings reported; recall is known bugs found divided by all known bugs. A useful evaluation reports both.
An LLM that plans, reads files, runs tools and tests, and iterates over a codebase, instead of answering one prompt over pasted code.
The model proposes harnesses, seeds or mutation patterns; the fuzzer supplies ground truth by crashing or violating an oracle.
The model generates properties, invariants or code; a solver or prover checks them, so the model's output is accepted only when a machine confirms it.
Confident, plausible findings that do not reproduce. The curl project ended its bug bounty on 31 January 2026 after confirmed-report rates fell below five percent.
The human time needed to validate candidate findings. zkSecurity reported more than a thousand candidates across two hundred projects as its main bottleneck; Codex Security reported more than ten thousand high findings in its first month.
The model has seen the audit report or the bug in training, so a benchmark hit measures recall of memory rather than discovery.
A named person validates exploitability, severity and disclosure before a finding is reported to a client or maintainer.
An AI-assisted audit is a human-signed report in which AI widened coverage; an AI audit is machine output with no accountable reviewer.
Repository contents (commit messages, comments, PR descriptions, issue text) can carry instructions that hijack an agent running with CI privileges.
The same coordinated-disclosure rules as human findings, plus a duty to send only validated, reproducible reports so maintainers are not flooded.
Running AI analysis repeatedly, on every commit and whenever models improve, on the premise that LLM bug finding is non-deterministic and coverage compounds with runs.
Tooling that turns a candidate finding into an executable exploit or failing test, such as zkao's cheatVM for Aleo or Codex Security's sandbox.
A scope document the tool reads before scanning (zkao's zkao.md, Codex Security's generated threat model) stating trust boundaries, adversaries and out-of-scope behaviour.
Whether the tool's assigned severities match what an expert would assign. In the CIRCL study, four of seven AI severities were too high and one critical was rated medium.
Asked every time
01Do AI tools actually find bugs in cryptographic code?
Yes, with public confirmation in 2026: zkao found seven bugs in Cloudflare's CIRCL, the critical OpenVM zkVM soundness bug CVE-2026-46669 and four zero-days in Bron Labs' library; AISLE was credited with all twelve OpenSSL CVEs in the January 2026 release; Codex Security reported OpenSSH and GnuTLS CVEs; Project Glasswing partners disclosed the wolfSSL certificate-forgery bug. General scanners find implementation bugs; only cryptography-specific harnesses have found protocol-level and soundness bugs. Permalink
02Which AI tool should I use on ZK circuits?
zkao (zkSecurity) for continuous scanning of Circom, Leo, Rust and Go cryptographic code with human validation available; zk-skills for a free first pass with your own agent on Circom; zkFuzz or zkCraft for execution-backed underconstraint detection. Solver-based tools such as Picus and Lean frameworks such as Clean are complementary, not AI, and are covered on the formal verification side. Permalink
03Can an AI audit replace a human audit?
Not in 2026. The best published numbers are 30 percent average recall against real human audits (AuditAgent), 55 percent precision in a controlled study (Sherlock AI), and about 20 percent of a top firm's reported bugs first surfaced by AI (Trail of Bits). Every vendor with results keeps a named human validating findings. AI widens coverage and lowers cost per candidate; a human still decides what is real, how severe it is, and what to disclose. Permalink
04How much does AI auditing cost?
Frontier-lab scanners bill as token usage or enterprise subscriptions. zkao's published tiers have median scan costs of about 49, 281 and 1,112 dollars by codebase size on prepaid credits. Savant Chat charges 0.07 to 0.50 dollars per line or 250 to 2,500 dollars per month. Open-source options (zk-skills, Buttercup, Hound, AIxCC systems) cost model usage and engineer time. The dominant cost is human triage of candidates. Permalink
05How do I reduce false positives?
Give the tool a threat model (zkao's zkao.md cut false positives from 14 of 33 findings to 2), prefer tools with execution-backed validation (sandboxed exploits, fuzzing oracles, PoC harnesses), require a second validating pass, deduplicate across runs, and have a human reproduce before anything is reported. Permalink
06Which benchmark numbers can I trust?
Numbers with a public dataset, a stated model cutoff, both precision and recall, and a full findings list. EVMbench is widely cited but OpenZeppelin found invalid items and contamination risk; zkbugs is the only ZK benchmark and its full-codebase mode is the harder, more honest number; Nethermind's recall on its own real audits is the most realistic figure published by a vendor. Permalink
07Is it safe to point an AI agent at my repository?
Only with isolation. Repository content can carry instructions that hijack an agent, and injection CVEs were found in Git tooling for agents in 2026. Run scanners with read-only, scoped credentials, no secrets in the environment, and ask the vendor how repository text is separated from agent instructions and which models see your code. Permalink
08Should I scan once or continuously?
Continuously, if the tool deduplicates. LLM findings are non-deterministic, models improve monthly, and code changes; zkao, Octane and Olympix are built around per-commit or re-triggered scans. A one-off AI scan before an audit is still worth doing, but treat it as a snapshot. Permalink