Title: Eyes-on-Me: Scalable RAG Poisoning through Transferable Attention-Steering Attractors
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: RAG, database poisoning, adversarial attack, LLM safety, attention-steering

Abstract: Existing data poisoning attacks on retrieval-augmented generation (RAG) systems scale poorly because they require costly optimization of poisoned documents for each target phrase. We introduce Eyes-on-Me, a modular attack that decomposes an adversarial document into reusable **Attention Attractors** and **Focus Regions**. Attractors are optimized to direct attention to the Focus Region. Attackers can then insert semantic baits for the retriever or malicious instructions for the generator, adapting to new targets at near zero cost. This is achieved by steering a small subset of attention heads that we empirically identify as strongly correlated with attack success. Across 18 end-to-end RAG settings (3 datasets $\times$ 2 retrievers $\times$ 3 generators), Eyes-on-Me raises average attack success rates from 21.9 to 57.8 (+35.9 points, 2.6$\times$ over prior work). A single optimized attractor transfers to unseen black box retrievers and generators without retraining. Our findings establish a scalable paradigm for RAG data poisoning and show that modular, reusable components pose a practical threat to modern AI systems. They also reveal a strong link between attention concentration and model outputs, informing interpretability research.

TL;DR: We introduce EYES‑ON‑ME, a modular RAG‑poisoning attack that learns reusable Attention Attractors to steer a small set of high‑correlation attention heads toward a swappable payload Focus Region, enabling near‑zero‑cost adaptation to new baits.