Divide and Doubt: Diverse Distributed Poisoning for Retrieval-Augmented Generation

Multi-passage corpus poisoning often repeats one target claim across similar documents, creating correlated lexical and semantic patterns that similarity- and conflict-aware defenses can suppress jointly. We introduce DnD Divide and Doubt, a targeted attack based on two principles: distributing...

Adversarial Vulnerabilities of Neural Biomarker Identification Systems

There is growing interest in the proposed use of EEG signals as biometric credentials, but thus far there has been little research on the reliability and security of such biometrics. Prior adversarial tests have focused on deep-learning classifiers and assumed attackers have full access to the...

MAI-2025-0002

Large Language Model LLM-based Multi-Agent Systems MAS are susceptible to intellectual property IP leakage attacks. These attacks can be executed by adversaries with black-box access, who interact solely through public APIs. By crafting adversarial queries, attackers can infiltrate the MAS,...

Unlearning Inversion Attacks for Graph Neural Networks

Graph unlearning methods aim to efficiently remove the impact of sensitive data from trained GNNs without full retraining, assuming that deleted information cannot be recovered. In this work, we challenge this assumption by introducing the graph unlearning inversion attack: given only black-box...