Studying Coordination and Collusion in Multi-Agent LLM Code Reviews

Jennifer Za, Aristeidis Panos, Roger Dearnaley, Samuel Albanie

Keywords: AI agents, LLMs, Collusion, Cooperation, Coding, Code-review, Backdoor, Control, AI Safety, Multi-agent systems, Attacks

TL;DR: We show that when incentivised, most frontier LLMs collude in multi-agent code review by submitting backdoored code and routing to allies, with GPT-5 as the exception, underscoring the need for coordination-aware oversight.

Abstract:

Agentic large language models (LLMs) are rapidly moving from single-assistant
tools to collaborative systems that write and review code, creating new failure
modes, as agents may coordinate to subvert oversight. We study whether such
systems exhibit coordination behaviour that enables backdoored code to pass peer-
review, and how these behaviours vary across seven frontier models with minimal
coordination scaffolding. Six of seven models exploited the backdoor incentive,
submitting functionally impaired code in 34.9-75.9% of attempts across 10 rounds
of our simulation spanning 90 seeds. Whilst GPT-5 largely refused (≤10%),
models across GPT, Gemini and Claude model families preferentially requested
reviews from other saboteurs (29.2–38.5% vs 20% random), indicating possible
selective coordination capabilities. Our results reveal collusion risks in LLM code
review and motivate coordination-aware oversight mechanisms for collaborative
AI deployments.

Submission Number: 232