AI Agents Match Human Hackers in Smart Contract Exploits, Generate $4.6M in Simulated Attacks

Reviewed byNidhi Govil

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Anthropic's research reveals that frontier AI models can autonomously exploit smart contracts with human-level capability, successfully attacking over half of recent blockchain vulnerabilities and discovering new zero-day flaws. The study demonstrates how AI agents generated $4.6 million in simulated exploit revenue while costs continue to decline.

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AI Models Achieve Human-Level Smart Contract Exploitation

Frontier artificial intelligence models have demonstrated the ability to autonomously exploit smart contracts with capabilities matching skilled human attackers, according to groundbreaking research released by Anthropic. The study evaluated ten leading AI models, including Claude Opus 4.5, Claude Sonnet 4.5, GPT-5, Llama 3, and DeepSeek V3, revealing that these systems successfully attacked 207 out of 405 historical smart contract vulnerabilities, generating $550 million in simulated stolen funds

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The research, conducted in collaboration with the Machine Learning Alignment & Theory Scholars Program, focused on real-world exploits that occurred across major blockchains between 2020 and 2025. Most significantly, AI agents exploited 17 of 34 smart contracts deployed after March 2025, draining $4.5 million in simulated funds and demonstrating their effectiveness against recently deployed systems

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Zero-Day Vulnerability Discovery

Beyond replicating known exploits, the AI models demonstrated their ability to discover previously unknown vulnerabilities. When tested against 2,849 recently deployed contracts on Binance Smart Chain with no known security issues, both GPT-5 and Claude Sonnet 4.5 uncovered two novel zero-day vulnerabilities. These discoveries generated $3,694 in simulated value at a total API cost of $3,476, with GPT-5 achieving profitability where the exploit value exceeded the computational costs

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One notable discovery involved a token contract with a public calculator function lacking a view modifier, allowing the AI agent to repeatedly manipulate internal state variables and sell inflated balances on decentralized exchanges. This simulated exploit alone generated approximately $2,500 in value

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Declining Costs and Increasing Efficiency

The research revealed troubling trends in the economics of AI-driven attacks. Analyzing four generations of Claude models, researchers found that the median number of tokens required to produce a successful exploit declined by 70.2%, significantly reducing operational costs. The average cost to scan a contract for vulnerabilities now stands at just $1.22, making large-scale automated scanning economically viable

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Anthropic€™s data shows that AI exploit capabilities improved dramatically over the past year, with agents progressing from exploiting 2% of post-March 2025 vulnerabilities to 55.88%. The total exploit revenue jumped from $5,000 to $4.6 million, with revenue doubling every 1.3 months as models improved and costs declined

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Implications for Blockchain Security

Security experts emphasize that these capabilities represent an acceleration of existing vulnerabilities rather than entirely new attack vectors. David Schwed, COO of SovereignAI, noted that AI is already being used in Application Security Posture Management tools and standard security scanners, meaning malicious actors will inevitably adopt the same technology. The automated nature of these attacks enables 24/7 vulnerability scanning across all projects, regardless of their total value locked

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The research demonstrates that AI agents can now automate tasks historically performed by skilled human hackers, including identifying bugs, generating complete exploit scripts, and sequencing transactions to drain liquidity pools. This automation extends beyond decentralized finance protocols to potentially affect traditional software and infrastructure supporting digital asset markets

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Defensive Applications and Future Outlook

Despite the concerning implications, Anthropic emphasized that the same AI capabilities enabling exploitation can be adapted for defensive purposes. The company plans to open-source its SCONE-bench dataset to help developers benchmark and harden smart contracts before deployment. Security experts suggest that with proper controls, rigorous internal testing, real-time monitoring, and circuit breakers, most vulnerabilities remain preventable

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The research underscores the critical need for proactive adoption of AI-powered defense systems. As Schwed noted, €œThe good actors have the same access to the same agents. So if the bad actors can find it, so can the good actors.€ However, the shrinking window between vulnerable contract deployment and potential exploitation means developers must act quickly to integrate automated security tools into their workflows

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