OpenAI, in collaboration with the investment firm Paradigm, confirmed the launch of EVMbench. This new assessment standard is designed to precisely measure the ability of AI agents to detect, exploit, and patch vulnerabilities in smart contracts on the Ethereum Virtual Machine (EVM).
This system uses a dataset of over 120 real-world vulnerabilities extracted from protocols that have suffered attacks in the past, testing the security of an ecosystem that currently safeguards over $100 billion in on-chain assets.
The introduction of EVMbench reflects a structural evolution observed between 2024 and 2025, where AI agents progressed from simple trading bots to autonomous systems capable of interacting with the composability of DeFi.
To understand the magnitude of this change, we must compare it to The DAO exploit in 2016. Back then, detecting and responding to the drain on funds took days of manual debate and intense human coordination.
With tools like EVMbench, a modern AI agent can identify attack vectors and execute a countermeasure (or an exploit) in milliseconds, operating at the speed of the network’s block generation. This quantitative difference marks the shift from security based on human oversight to one based on algorithmic defense.
A structural paradigm shift in AI Agents
One of the key points is the redefinition of Security Auditing and Costs. Traditionally, protocol security relied on human auditing firms whose services could cost up to $150,000 and take weeks.
The democratization of tools like EVMbench allows AI agents to perform comprehensive scans in milliseconds for a fraction of the computational cost (estimated at less than $1 per run). This enables small protocols to have high-level security, but it also gives attackers with limited resources a “supercomputer for exploits.”
On the other hand, we must also consider the emergence of “Non-Human Economic Actors”: We are witnessing the birth of a new category of financial participants. Blockchain allows these agents to operate wallets and execute strategies without KYC or human intervention.
We are also heading towards a “Code Arms Race,” since, unlike other sectors, in crypto, code is money. EVMbench allows for the training of offensive AIs capable of launching massive automated attacks. This will force developers to integrate defensive AIs into the very core of their smart contracts to patch errors in real time before an attacker can detect them. This is not just a technical shift; it is a transition towards autonomous markets where humans and machines share control of capital.
Preliminary tests indicate that advanced models have managed to develop exploits worth $4.6 million, demonstrating that AI can already behave like a sophisticated, state-level attacker.
This phenomenon could even redefine MEV (Maximum Extractable Value). Network validators could begin using agents trained with EVMbench to identify malicious transactions in the mempool and front-run them to protect the user or capture the profit, making security a native business model of the network.
Conclusion
The success of this temporary OpenAI protocol will be measured by the ability of Ethereum developers to integrate these tools before malicious actors do. What will validate this trend will be the volume of funds recovered by automated white-hat attacks compared to funds lost in conventional attacks over the next six months.
