How SW and HW Vulnerabilities Can Complement LLM-Specific Algorithmic Attacks (UT Austin, Intel et al.)

A technical paper titled “Cascade: Composing Software-Hardware Attack Gadgets for Adversarial Threat Amplification in Compound AI Systems” by UT Austin, Intel Labs, Symmetry Systems, Microsoft, and Georgia Tech explores how software and hardware vulnerabilities can combine with LLM-specific algorithmic attacks to compromise the integrity of compound AI pipelines. The paper demonstrates two novel attacks that leverage system-level vulnerabilities along with algorithmic weaknesses to breach AI safety and confidentiality. By systematically analyzing attack primitives and mapping vulnerabilities to different stages of an attack lifecycle, the paper emphasizes the importance of addressing traditional vulnerabilities for robust defense strategies in the future.