Can AI Agents Make Ethereum Safer? OpenAI and Paradigm Launch EVMbench
ChatGPT creator OpenAI and crypto-focused investment firm Paradigm have jointly unveiled EVMbench, a specialized testing framework built to probe how well AI agents can secure Ethereum smart contracts. The initiative targets one of the most pressing problems in Web3: preventing catastrophic flaws in the code that runs decentralized applications.
What Is EVMbench?
EVMbench is a benchmark environment tailored for the Ethereum Virtual Machine (EVM). Its purpose is to measure how effectively AI systems can:
– Detect high-severity vulnerabilities in smart contracts
– Propose and apply patches
– In some cases, exploit those same vulnerabilities, to prove they are real and not false positives
In other words, EVMbench is not just checking if an AI can “spot bugs” in theory. It tests whether an agent can behave like a full-stack security researcher: identify a weakness, understand its impact, and demonstrate a working exploit or a correct fix.
Why Focus on the Ethereum Virtual Machine?
Smart contracts form the execution layer of the Ethereum network. They handle:
– Decentralized finance (DeFi) operations like lending, trading, and derivatives
– Token launches and governance systems
– NFT marketplaces and gaming economies
– Infrastructure primitives such as bridges and staking protocols
Because this logic is on-chain and often immutable once deployed, any critical bug can immediately put user funds at risk. According to on-chain analytics, the weekly number of smart contracts deployed on Ethereum hit a record 1.7 million in November 2025, with 669,500 contracts deployed just last week. That volume magnifies the challenge: human auditors alone cannot feasibly review everything.
EVMbench aims to explore whether AI can meaningfully close this capacity gap.
A Curated Corpus of Real-World Vulnerabilities
Rather than using synthetic or toy examples, EVMbench is built on a library of real-world smart contract flaws. The benchmark currently includes:
– 120 carefully selected vulnerabilities
– Drawn from 40 professional audits
– With many sourced from open audit competitions such as Code4rena
These are not trivial mistakes; they are high-impact issues that, in production, could (or did) lead to serious financial loss. By grounding the benchmark in historical audit findings, OpenAI and Paradigm ensure that models are tested against problems that matter in practice: reentrancy, access control failures, broken invariants, faulty math, oracle manipulation, and more.
From Detection to Exploitation and Patching
Traditional static analyzers and linters can highlight suspicious patterns in smart contracts, but they often struggle with context: which issues are actually exploitable, and what is the worst-case outcome?
EVMbench pushes AI agents further:
– Detection: Can the model locate the precise section of code where the vulnerability resides?
– Exploitation: Can it craft an attack transaction or sequence that reliably triggers the bug?
– Patching: Can it modify the contract logic to close the vulnerability without breaking legitimate functionality?
This end-to-end evaluation is crucial. A tool that flags everything as dangerous is unusable; a tool that can produce a proof-of-concept exploit and then propose a minimal, correct fix becomes genuinely valuable to auditors and developers.
Why AI Is a Natural Fit for Smart Contract Security
Smart contract reviews blend several demanding skills:
– Deep understanding of EVM execution semantics
– Familiarity with Solidity (or other EVM languages) and common design patterns
– Ability to reason about complex state transitions and edge cases
– Awareness of real-world attack techniques and historical exploits
Modern AI models, especially those tuned for code, are well-equipped to:
– Read and summarize large codebases
– Compare similar contracts and spot unusual deviations
– Reason step-by-step about state changes over multiple transactions
– Generate human-readable explanations of subtle logic errors
EVMbench gives researchers a way to quantify these abilities, rather than relying on anecdotal success stories. It creates a reproducible standard to track whether new versions of AI agents are actually getting better at protecting Ethereum.
Potential Use Cases in the Ethereum Ecosystem
If AI agents consistently perform well on EVMbench, several practical applications become realistic:
1. Pre-deployment checks
Projects could run their contracts through an AI “security gate” before launch, catching obvious issues early and cheaply.
2. Assistance for human auditors
Auditors could use AI models as co-pilots: the agent surfaces suspicious flows and candidate exploits, while humans validate, prioritize, and finalize reports.
3. Continuous monitoring of deployed contracts
Even after deployment, AI tools could scan for newly discovered vulnerability classes or dangerous upgrade patterns in proxy-based systems.
4. Security education and onboarding
Learning materials could be built around EVMbench-style challenges, helping junior developers practice spotting and fixing real vulnerabilities with AI-powered feedback.
5. Automated patch suggestions for legacy code
Many older contracts have known risks but no clear remediation plan. AI systems trained and evaluated via EVMbench might propose incremental, safer upgrade paths.
Limits and Risks of Relying on AI
Despite the promise, there are serious caveats:
– False sense of security: Passing EVMbench is not a guarantee that a contract is safe. Novel vulnerabilities, new DeFi primitives, and composability risks may not resemble past issues.
– Model hallucinations: AI can generate plausible but incorrect code fixes or misinterpret complex logic, especially when contracts use unconventional patterns.
– Adversarial pressure: As security tools improve, attackers may attempt to design exploits that evade AI-based detectors, creating an arms race.
– Data coverage: EVMbench is based on 120 vulnerabilities-substantial, but still a snapshot of a much wider threat landscape.
For these reasons, AI agents are best viewed as powerful assistants, not replacements for rigorous security processes and expert review.
How EVMbench Could Shape Future AI Models
Benchmarks determine what researchers optimize for. By publishing a task suite focused on real EVM vulnerabilities, OpenAI and Paradigm are implicitly encouraging:
– Better reasoning over execution traces and state changes
– Improved understanding of EVM bytecode and low-level opcodes
– Models that can handle multi-contract systems, proxies, and upgradeable patterns
– Training on realistic exploit paths, not just static code smells
Over time, we might see specialized “security-tuned” AI agents whose primary training objective is to excel on benchmarks like EVMbench and successors. That could lead to an ecosystem of domain-specific models embedded directly in development environments, CI/CD pipelines, and auditing workflows.
Implications Beyond Ethereum
Although EVMbench is tailored to the Ethereum Virtual Machine, its underlying concept is transferable:
– Other EVM-compatible chains (like L2 rollups or sidechains) share similar vulnerabilities, so advances here propagate across much of the blockchain landscape.
– Non-EVM platforms can adapt the idea: curated vulnerability corpora, exploit-and-patch evaluation, and AI agents as security co-pilots for their own smart contract languages.
The broader takeaway is that AI can be systematically tested and improved as a security tool, rather than treated as a black box.
The Road Ahead: Collaboration Between Humans and Machines
The explosion in smart contract deployments-millions per month at peak-makes purely manual security review untenable. EVMbench represents a structured attempt to see how far AI can carry part of this load.
In the near term, the most realistic scenario is a hybrid model:
– AI agents, evaluated and iteratively improved through tools like EVMbench, handle broad, initial sweeps of contract code, generating candidate findings and patches.
– Human experts focus their time on validating critical issues, understanding complex protocol interactions, and designing robust mitigations.
If this collaboration works, the net result could be an Ethereum ecosystem where security reviews are faster, more consistent, and more widely accessible-even to smaller teams that cannot afford full-scale audits for every deployment.
Whether AI will ultimately become a standard, trusted component of Ethereum’s security stack depends on how it performs on real benchmarks under real pressure. EVMbench is one of the first serious attempts to measure that performance in a disciplined way.