CyberDual: AI Attackers versus AI Code Reviewers in Real Software Maintenance

Chenyuan Yang, Hwiwon Lee, Jongseong Kim, Yihan Yang, Dongjun Kim +2

Open-source projects now let AI agents review incoming code, but the code under review may itself come from an AI attacker. We present CyberDual, an arena for this dual role of AI: attacker agents hide an exploitable bug in real maintenance work on widely used open-source projects, and reviewer agents try to find and repair it. Execution decides each match: we rerun the attacker's proof of concept (PoC) on the code that comes out of review. Every attack is new, and the windows can be rebuilt each quarter from the latest upstream history. Three agentic reviewers catch 85-91% of 1,088 valid attacks from six attackers. Yet when the attacker spreads the bug over real upstream commits, about one in six attack slots still ends with a working exploit after review. The strongest attacker has open weights: GLM 5.3 almost never refuses, and its attacks make up 13 of the 28 that get past all three reviewers. When they miss, Claude reviewers mostly approve the attack, while GPT-6-Sol mostly repairs the wrong code; a reviewer from a second vendor catches about two thirds of a Claude reviewer's misses. Beyond a leaderboard, CyberDual has three uses. First, PoC execution gives both sides a…