We present the first systematic study of the resilience of large language model (LLM) agents to random hardware-induced faults. Prior work on hardware fault resilience in LLMs focuses on model-level outcomes, such as incorrect outputs or generation failures. Yet LLM agents repeatedly interact with tools and environment...
Aiden Rubey, Zachary Coalson, Cong Chen et al.· 0 citations
Modern AI systems are designed to refuse hazardous requests. A jailbreak is a prompt crafted to bypass those safeguards and elicit outputs that the system would normally refuse to provide. The MLCommons Jailbreak Benchmark v1.0 provides an end-to-end methodology for evaluating the robustness of large language models to...
C. Maple, Cagatay Yucel, Isaac Holeman et al.· 0 citations
Several open research questions are identified, including how to efficiently record incidents and how to determine whether vulnerabilities and incidents generalize, and privacy requirements are summarized and research directions for the secure and trustworthy deployment of AI agents are outlined.
Anastasia Pustozerova, E. Bagdasarian, Luca Beurer-Kellner et al.· 0 citations
This work proposes an efficient and effective adversarial attack detection scheme leveraging the multi-task perception within a complex vision system, and develops a consistency score metric to measure the inconsistency between vision tasks.
Cong Chen, J. Monteuuis, Jonathan Petit· 0 citations
(k)-SwordStamp is designed: semantic watermarks with order-robust detection over sub-sentence units, reducing sensitivity to attacker-chosen structure at a small quality cost.
Abdulrahman Diaa, Jonathan Petit, Florian Kerschbaum· 2 citations
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