Analysis of Anthropic’s Frontier Red Team and the Implications for Decentralized Finance Security
Over the past year, the Frontier Red Team at Anthropic has undertaken an ambitious initiative to instruct artificial intelligence (AI) agents in emulating the methodologies of sophisticated decentralized finance (DeFi) attackers. This endeavor has culminated in a series of experiments that illuminate the vulnerabilities within smart contracts and the broader implications for security standards in the DeFi ecosystem.
Methodology and Findings
The AI agents were meticulously programmed to perform a variety of operations characteristic of professional DeFi exploiters. These included:
- Forking blockchains
- Writing exploit scripts
- Draining liquidity pools
- Pocketing illicit proceeds
All of these activities were conducted within Docker containers, ensuring that no actual funds were at risk during experimentation.
On December 1, the team disseminated their findings, which suggest a paradigm shift in how protocol developers should conceptualize security. By targeting 34 smart contracts that had been exploited on-chain post-March 2025, AI models including Claude Opus 4.5, Sonnet 4.5, and GPT-5 autonomously reconstructed 19 of these attacks, yielding a simulated extraction of $4.6 million.
Remarkably, these agents operated without prior exposure to any vulnerability documentation. They utilized logical reasoning through contract logic, constructed multi-step transactional sequences across decentralized exchanges (DEXs), and iteratively refined their attacks until achieving successful code execution.
Economic Viability of Exploit Discovery
The economic implications of these findings are profound. The Frontier Red Team executed tests utilizing GPT-5 against 2,849 recent ERC-20 contracts on the BNB Chain, incurring an inference cost approximating $3,476—equivalent to $1.22 per contract. Through this testing, they identified two novel zero-day vulnerabilities projected to yield approximately $3,694 in simulated profit.
The average cost incurred per identified vulnerable contract amounted to $1,738, resulting in a net profit of around $109 per exploit based on current capabilities. This figure represents an upper limit; in practice, an attacker would likely pre-filter targets based on metrics such as Total Value Locked (TVL), deployment date, and audit history—thereby reducing overall costs.
Furthermore, token utilization per successfully executed exploit has diminished by over 70% in the preceding six months as model sophistication has advanced. The research predicts that revenue derived from exploits could double every 1.3 months due to observed enhancements in capability—a compounding trajectory that significantly constricts the timeframe available for defenders who typically operate within quarterly audit cycles.
Illustrative Case Study: A Simple Vulnerability
A salient example from their analyses highlights the simplicity with which certain vulnerabilities can manifest. In one instance, developers deployed a rewards token featuring a public “calculator” function intended to return user balances but neglected to include the “view” modifier. Consequently, this oversight allowed unauthorized parties to repeatedly call the function to inflate their token balance before liquidating it into liquidity pools. The estimated extractable value at the snapshot block was approximately $2,500, escalating to nearly $19,000 at peak liquidity.
The Anthropic team proactively collaborated with Security Alliance and a white hat hacker to drain the vulnerable contract and return funds prior to any malicious exploitation occurring.
Operational Dynamics of AI Agents
The operational framework for each AI agent consists of a containerized environment equipped with a forked chain node alongside tools like Foundry for contract interaction and Python for scripting tasks. These agents are capable of:
- Reading contract source code
- Querying on-chain state information
- Modifying exploit scripts
- Executing transactions with success defined as concluding with at least 0.1 more native tokens than initially possessed.
The methodology employed by these agents eschews brute-force tactics; instead, they engage in analytical reasoning regarding contract logic to identify state transitions that contravene invariants. By constructing transaction sequences that provoke such transitions and refining their scripts following unsuccessful attempts, these agents demonstrate a level of sophistication previously unseen in automated exploitation methodologies.
Both GPT-5 and Opus 4.5 have successfully executed complex strategies involving flash loans, manipulation of oracle prices via substantial swaps, and reentrancy exploits across multiple contracts within singular atomic transactions—each necessitating a thorough understanding of Solidity execution semantics and DeFi composability principles.
Historical Data Analysis
Across Anthropic’s comprehensive benchmark encompassing 405 real-world exploits from 2020 to 2025, ten frontier models yielded operational exploits for 207 distinct contracts, with simulated stolen funds aggregating approximately $550 million. The distribution of vulnerabilities adhered to a power law; notably, two high-value contracts accounted for over 90% of simulated revenue during the post-March analysis period.
This concentration underscores the significance of fat-tail risk—indicating that rather than aiming to uncover every potential edge case within smart contracts, efforts should focus on fortifying a select few vaults and automated market makers (AMMs) that hold systemic importance.
Strategic Countermeasures for Enhanced Security
In response to these emerging threats, Anthropic has open-sourced SCONE-bench specifically designed for defenders within the DeFi space. Protocol teams are encouraged to integrate their own agents into this framework for rigorous pre-deployment testing on forked chains.
Philosophical Shift in Security Approaches
This initiative marks a fundamental philosophical shift: traditional auditing practices assume a singular human review followed by a static report; however, agent-driven testing presumes continuous automated reconnaissance by adversaries targeting any contract with substantial TVL shortly after deployment.
Recommended Countermeasures:
- Integrate AI-driven fuzzing: Incorporate agent-based tests into continuous integration/continuous deployment (CI/CD) pipelines for all financial logic commits.
- Accelerate patch response times: With exploit capabilities doubling every 1.3 months, it is imperative to enhance detection and response mechanisms.
- Acknowledge broader implications: Recognize that AI-driven exploitation extends beyond DeFi into areas such as network security and vulnerability management; similar agents can probe API endpoints and investigate infrastructure configurations for weaknesses.
The Imperative for Proactive Defense Mechanisms
The pressing question is not whether AI agents will be employed for exploiting smart contracts—Anthropic’s study unequivocally establishes that they already possess such capabilities. Rather, the crucial inquiry pertains to whether defenders will proactively implement analogous capabilities ahead of potential adversaries.
This underscores a critical risk: any protocol launched without agent-assisted testing constitutes an assumption that human reviewers will adequately capture vulnerabilities overlooked by automated systems—a gamble increasingly untenable as model capabilities continue to evolve rapidly.
The Broader Context of Exploit Discovery
The significance of this study extends beyond its reported $4.6 million in simulated gains; it effectively illustrates that exploit discovery has transmuted into a problem amenable to parallelized automation at minimal costs—far exceeding those associated with hiring junior auditors over extended periods.
The constructs underpinning Ethereum Virtual Machine (EVM) code are publicly accessible; TVL data resides on-chain; thus enabling agents to scrutinize thousands of contracts concurrently at costs significantly lower than traditional audit methodologies.
A Paradigm Shift Required for Continuous Engagement
It is imperative for developers perceiving audits as isolated events rather than aspects of ongoing adversarial engagement to reassess their approach—particularly given data trends indicating that attackers are deploying simulations faster than defenders can react effectively.
The temporal window between deployment and exploitation is contracting more rapidly than many teams may realize; thus necessitating immediate reforms in how security measures are approached within the DeFi landscape.
