CLASP: Defending Hybrid Large Language Models Against Hidden State Poisoning Attacks

arXiv 2603.12206·high signal

Introduces the first defense against Hidden State Poisoning Attacks (HiSPAs) in Mamba/SSM hybrid architectures — adversarial strings that corrupt SSM memory to hijack model behavior. CLASP frames mitigation as token-level binary classification and achieves high detection rates without degrading model performance. As hybrid SSM-Transformer models enter production (lower latency, linear complexity), this is an immediately actionable security concern for teams deploying Mamba-family models.