Insights on real exploits, token risk, and AI systems that test security through execution — not static analysis.
When Coinbase announced users could trade millions of ERC-20 DEX tokens directly from their app, it marked a major moment for decentralized access — one that didn't compromise on safety. TestMachine's Predator quietly powered the new layer of token safety behind the scenes, achieving 100% accuracy with zero false positives.
"Predator stood out because it achieved 100% accuracy on all tokens, with no false positives or negatives, even catching human errors in our own reviews."Read the full story →
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We ran Azimuth against Zellic's V12 on the same Code4rena codebase. Azimuth matched 100% of critical findings and 92% of validated highs — with 40% fewer total flags.
Read more →Azimuth achieved a 91% validation success rate on EVMBench Exploit, validating 20 out of 22 findings with automated validation scripts.
Read more →AI-generated "slop" is overwhelming bug bounty programs. THORChain and Code4rena shut down. Real exploits still happen. How verification gaps let critical bugs slip through.
Read more →Azimuth achieves 78.6% recall on EVMBench, outperforming AuditAgent (67%), Guardix (59.8%), and base models. How state space exploration beats static analysis.
Read more →Azimuth caught 70% of real DeFi exploits worth $1.12 billion—including Euler's $197M hack. AI doesn't replace auditors. But you can't ignore it anymore.
Read more →We tested CertiK AI and TestMachine on two different smart contract systems to understand how each tool interprets vulnerabilities. See how bug detection differs from exploit modeling.
Read more →Wondering if AI will replace smart contract auditors? Discover how AI-powered vulnerability detection and automated smart contract scanners are transforming DeFi security by extending human expertise rather than replacing it.
Read more →TestMachine is now competing publicly on AgentArena—a platform where AI audit agents go head-to-head on real smart contract challenges. Full transparency, real results.
Read more →Learn why traditional smart contract audit tools can't protect against post-deployment vulnerabilities. Discover how AI-powered automated smart contract scanners using reinforcement learning detect exploits that manual audits miss.
Read more →The Resolv exploit reveals a deeper issue in Web3: permission risk. When incidents like the recent Resolv exploit occur, the instinct is to search for the point of failure. But in many cases, nothing actually breaks. The system behaves exactly as it was designed to.
Read more →We compared AuditAgent and Azimuth across four real codebases. Static AI auditors catch common patterns, but behavioral analysis uncovers the multi-step exploits and economic attacks that matter most.
Read more →A growing body of peer-reviewed research confirms what we've long argued: LLMs achieve as low as 22.6% precision and 13% recall on modern smart contracts. TestMachine's execution-driven approach eliminates false positives entirely by proving exploits, not guessing at them.
Read more →TestMachine successfully replicated the Euler Finance hack, demonstrating its ability to identify complex vulnerabilities that eluded human auditors using multi-agent simulation and reinforcement learning.
Read more →TestMachine uncovered a severe vulnerability in the Virtuals AI Agent protocol that could allow an attacker to artificially inflate token balances to near-maximum values, posing a significant risk to users.
Read more →TestMachine has raised $6.5M to scale its AI-driven platform analyzing 1M+ tokens and delivering real-time blockchain security for exchanges, developers, and DeFi.
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