What is EVMbench? The New AI Standard for Smart Contract Security

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Key Takeaways

  • AI Exploitation Outpaces Defense: Early results show a "security gap." OpenAI’s GPT-5.3-Codex achieved a staggering 72.2% success rate in exploit mode, but only fixed about 41.5% of those same bugs correctly. AI is currently a better hacker than it is a doctor.
  • Real-World Stakes: Unlike synthetic benchmarks, EVMbench uses production-grade code, including complex scenarios from the Tempo blockchain. This ensures the AI is being tested on "live-fire" scenarios where logic errors can lead to millions in losses.
  • A Defensive Call to Action: Along with the benchmark, OpenAI committed $10 million in API credits for defensive cybersecurity research. The goal is to ensure that as AI grows more powerful, the "good guys" have the tools to build AI-driven automated auditors that can keep pace with AI-driven attackers.

What is EVMbench? The New AI Standard for Smart Contract Security

In the rapidly evolving world of Web3, security is no longer just a human endeavor. On February 18, 2026, OpenAI and Paradigm announced the launch of EVMbench, an open-source benchmarking framework designed to evaluate how AI agents handle the high-stakes world of Ethereum smart contract security.
As AI models like GPT-5.3-Codex become increasingly capable of writing and executing code, the industry needs a way to measure whether these agents are becoming better defenders or more dangerous attackers.

How EVMbench Works?

EVMbench isn't just a simple quiz; it's a rigorous, sandboxed stress test.() It uses a dataset of 120 high-severity vulnerabilities pulled from 40 real-world audits and security competitions (such as Code4rena).
The framework evaluates AI models across three distinct "Modes" that mirror a professional security auditor's workflow:
  1. Detect Mode (The Auditor)

The AI is given a smart contract repository and tasked with finding specific "ground-truth" vulnerabilities. Success is measured by recall—how many real bugs did the AI catch compared to the human experts who originally audited the code?
  1. Patch Mode (The Engineer)

Once a bug is found, can the AI fix it? In this mode, the agent must modify the code to remove the vulnerability.() However, there’s a catch: the "patch" must preserve original functionality.() If the AI fixes the bug but breaks the contract’s primary features, it fails.
  1. Exploit Mode (The Red Teamer)

This is the most "realistic" setting. In a local, sandboxed Ethereum environment (using a tool called Anvil), the AI must successfully execute a fund-draining attack. The benchmark programmatically checks if the "attacker" actually succeeded in moving simulated funds.

FAQs for EVMbench

Does EVMbench use real money or live networks?

No. EVMbench runs in a completely isolated, local environment. It uses a "containerized" version of the Ethereum Virtual Machine, meaning AI agents can attempt to "drain funds" without any real-world financial risk or legal consequences.

Why did OpenAI and Paradigm release this?

To create a "standardized yardstick" for AI security. By open-sourcing the benchmark, they are allowing the entire crypto community to track AI capabilities and encouraging developers to build AI-assisted auditing tools before malicious actors can weaponize the technology.

Can AI agents now replace human smart contract auditors?

Not yet. While AI is excellent at finding specific "needle-in-a-haystack" bugs when given hints, it still struggles with comprehensive audits of entire ecosystems. Human oversight is still the "final boss" of smart contract security.

What is the "Vibe-Coding" risk mentioned in these reports?

"Vibe-coding" refers to developers using AI to generate code quickly and deploying it without deep manual review. Recent exploits (like the $1.78M Moonwell incident) show that when humans "rubber-stamp" AI code too fast, critical logic errors can slip through to the mainnet.

How can I use EVMbench to test my own AI agents?

The entire framework is open-source and available on GitHub. Developers can download the dataset, set up a local Docker/Anvil environment, and run their own agents through the Detect, Patch, and Exploit pipelines.
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