Paper-Conference

Through introducing a strong, new SEO attack ⛽💡, we extensively evaluate widely-used embedding-based retrievers’ susceptibility to SEO attacks via corpus poisoning, linking it to key properties in embedding space.

We propose an efficient attack against neural tabular classifiers for automatic robustness evaluation, addressing attacker objectives such as feasibility (via incorporation of database constraints) and cost-efficiency.