Analyzing Adversarial Attacks on Sequence-to-Sequence Relevance Models
<|reference_start|>Analyzing Adversarial Attacks on Sequence-to-Sequence Relevance Models: Modern sequence-to-sequence relevance models like monoT5 can effectively capture complex textual interactions between queries and documents through cross-encoding. However, the use of natural language tokens in prompts, such as Query, Document, and Relevant for monoT5, opens an attack vector for malicious documents to manipulate their relevance score through prompt injection, e.g., by adding target words such as true. Since such possibilities have not yet been considered in retrieval evaluation, we analyze the impact of query-independent prompt injection via manually constructed templates and LLM-based rewriting of documents on several existing relevance models. Our experiments on the TREC Deep Learning track show that adversarial documents can easily manipulate different sequence-to-sequence relevance models, while BM25 (as a typical lexical model) is not affected. Remarkably, the attacks also affect encoder-only relevance models (which do not rely on natural language prompt tokens), albeit to a lesser extent.<|reference_end|>
@article{parry2024analyzing,
    title={Analyzing Adversarial Attacks on Sequence-to-Sequence Relevance Models},
    author={Andrew Parry, Maik Fr"obe, Sean MacAvaney, Martin Potthast, Matthias
  Hagen},
    journal={arXiv preprint arXiv:2403.07654},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.07654},
    primaryClass={cs.IR}
}