AI Auditing
AI auditing uses artificial intelligence to automate parts of the smart contract security review process, including property generation, coverage analysis, and vulnerability detection, producing executable test suites rather than just text-based findings.
In Depth
AI auditing in the smart contract context goes beyond running a language model over source code. Effective AI auditing combines automated property generation, coverage-guided fuzzing, and semantic analysis to systematically test every execution path through a protocol. The AI generates invariant properties specific to the target codebase, identifies coverage gaps, and enriches the test suite with semantic dimensions like truncation boundaries and reentrancy surfaces. The key differentiator from traditional automated tools is that AI auditing produces executable, verifiable artifacts — test suites that can be run, measured, and extended — rather than probabilistic text-based opinions about code safety.
Frequently Asked Questions
What is AI auditing for smart contracts?
AI auditing for smart contracts uses artificial intelligence to automate security analysis. This includes generating invariant properties, identifying coverage gaps, and running fuzzing campaigns. Unlike simple AI code review, effective AI auditing produces executable test suites that provide measurable, reproducible security assurance.
Is AI auditing better than manual auditing?
AI auditing and manual auditing are complementary. AI excels at systematic enumeration — generating properties, measuring coverage, and testing millions of transaction sequences. Human auditors excel at judgment — evaluating whether edge case behavior is intended, assessing economic attack viability, and reviewing business logic assumptions. The best results come from combining both.
How does Recon use AI in smart contract audits?
Recon uses AI to automatically generate invariant properties for target protocols, identify untested coverage classes, and enrich test suites with semantic analysis (truncation, overflow, and reentrancy boundaries). The AI produces a complete Chimera-based test suite that is then run as a full stateful fuzzing campaign and reviewed by human auditors.