Automated Exploit Validation: Your AI Agent vs a Real $107K DeFi Hack
We connected Azimuth to an AI agent over MCP and gave it one contract from a real exploit. It found and validated the vulnerability on its own. Here's how.
Read more →Beyond the Audit
Insights on real exploits, token risk, and AI systems that test security through execution — not static analysis.
Featured Story
Inside Coinbase's DEX Expansion: A Conversation with Coinbase Security on How TestMachine Powers Token Safety
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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90% Precision. 70% Recall: Validating Azimuth against real Sherlock judge verdicts.
Azimuth achieved 90% precision and 70% recall validating 552 real Sherlock audit findings. How automated triage solves Web3's spam crisis.
Read more →The Sherlock Bountiability Benchmark: What 13,000 Audit Submissions Actually Look Like
What public Sherlock contest data tells us about the LLM-era submission flood — and the benchmark we are building to measure both bug-finding and bug validation.
Read more →Azimuth Validation: Don’t Just Detect Vulnerabilities. Prove Them.
Azimuth Validation doesn’t ask if a vulnerability is exploitable — it tries to exploit it. Execution-based validation for smart contract security findings. 91% success rate on EVMBench.
Read more →Zellic’s V12 vs TestMachine: Specificity vs Sensitivity in AI Smart Contract Security Tools.
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 on EVMBench Exploit: 91% Validation Success Rate
Azimuth achieved a 91% validation success rate on EVMBench Exploit, validating 20 out of 22 findings with automated validation scripts.
Read more →Bug Bounty Programs and AI-Generated Vulnerabilities: The Fog of Slop Problem
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 on EVMBench: What 86.3% Recall Actually Means
Azimuth achieves 86.3% recall on EVMBench, outperforming GPT-5.6 Codex (61.54%), AuditAgent (67%), Guardix (59.8%), and base models. How state space exploration beats static analysis.
Read more →Azimuth vs $1B in Real DeFi Hacks. Here's What Happened.
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 →CertiK vs TestMachine: How AI Security Tools Interpret Smart Contract Vulnerabilities
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 →Will AI Replace Smart Contract Auditors? How AI-Powered Vulnerability Detection Is Transforming DeFi Security
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 →AI Smart Contract Auditing: Why We're Competing Publicly on AgentArena
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 →Smart Contract Audits Are the Foundation, Not The Finish Line: Why Automated Security Tools Are Essential in 2026
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 Hack Wasn't About Compromised Keys — It Was About Granted Permissions
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 →TestMachine Vs Large Language Models
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 →AuditAgent vs Azimuth: Static AI vs Behavioral Analysis in Smart Contract Security
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 →TestMachine Vs Euler: Recreating a $196 Million Exploit
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 →Breaking Virtuals Tokenomics: TestMachine Discovers a Critical Vulnerability in the Virtuals AI Agent Protocol
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 Secures Over $6.5M In Venture Funding
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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