Poisoning Retrieval Corpora by Injecting Adversarial Passages
This exposes a critical security flaw in widely used dense retrievers, posing risks for real-world applications like financial or forum search, and is incremental in benchmarking multiple systems.
The paper tackles the vulnerability of dense retrieval systems by proposing a novel attack that injects adversarial passages into retrieval corpora, showing it can mislead >94% of out-of-domain queries with only 50 passages and affect all tested systems with up to 500 passages.
Dense retrievers have achieved state-of-the-art performance in various information retrieval tasks, but to what extent can they be safely deployed in real-world applications? In this work, we propose a novel attack for dense retrieval systems in which a malicious user generates a small number of adversarial passages by perturbing discrete tokens to maximize similarity with a provided set of training queries. When these adversarial passages are inserted into a large retrieval corpus, we show that this attack is highly effective in fooling these systems to retrieve them for queries that were not seen by the attacker. More surprisingly, these adversarial passages can directly generalize to out-of-domain queries and corpora with a high success attack rate -- for instance, we find that 50 generated passages optimized on Natural Questions can mislead >94% of questions posed in financial documents or online forums. We also benchmark and compare a range of state-of-the-art dense retrievers, both unsupervised and supervised. Although different systems exhibit varying levels of vulnerability, we show they can all be successfully attacked by injecting up to 500 passages, a small fraction compared to a retrieval corpus of millions of passages.