XOXO: Stealthy Cross-Origin Context Poisoning Attacks against AI Coding Assistants
Adam Štorek Columbia University, Mukur Gupta, Noopur Bhatt University of Pennsylvania, University of Pennsylvania and Leidos Inc., Aditya Gupta, Janie Kim Google, Prashast Srivastava Amazon, Suman Jana , Columbia University
AI coding assistants automatically gather context from potentially untrusted sources to generate code recommendations. We introduce Cross-Origin Context Poisoning (XOXO), a novel attack that exploits this automatic context inclusion by subtly manipulating code without changing its semantics. Attackers introduce semantics-preserving transformations (e.g., renamed variables) to shared code, causing AI assistants to unknowingly recommend vulnerable code patterns to victims. To systematically identify effective transformations, we present Greedy Cayley Graph Search (GCGS), a black-box algorithm that efficiently composes transformations to identify adversarial inputs. Our evaluation demonstrates XOXO’s effectiveness at making LLMs generate buggy and vulnerable code, achieving average attack success rates of 73.20% against eight state-of-the-art models including GPT 4.1 and Claude 3.5 Sonnet v2, with vulnerability injection rates up to 66.67%. We also demonstrate a real-world attack against GitHub Copilot, highlighting critical security gaps in current AI coding tools.
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Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表