5 papers
PIPES: Securing Agent Perception with Provenance and Priors
Sanjay Kariyappa, Severin Klingler, G. Edward Suh
Tool-using agents consume external data from sources with different levels of trust, yet tool responses rarely identify who produced each component or what it should convey. We sho…
SideQuest: Model-Driven KV Cache Management for Long-Horizon Agentic Reasoning
Sanjay Kariyappa, G. Edward Suh
Long-running agentic tasks, such as deep research, require multi-hop reasoning over information distributed across multiple webpages and documents. In such tasks, the LLM context i…
A Prior-Aware Metric for Efficiently Distinguishing Memorization from Generalization in Large Language Models
Trishita Tiwari, Ari Trachtenberg, G. Edward Suh
Training data leakage from Large Language Models (LLMs) raises serious concerns related to privacy, security, and copyright compliance. A central challenge in assessing this risk i…
ReasoningBomb: A Stealthy Denial-of-Service Attack by Inducing Pathologically Long Reasoning in Large Reasoning Models
Xiaogeng Liu, Xinyan Wang, Yechao Zhang +5
Large reasoning models (LRMs) extend large language models with explicit multi-step reasoning traces, but this capability introduces a new class of prompt-induced inference-time de…
ReasAlign: Reasoning Enhanced Safety Alignment against Prompt Injection Attack
Hao Li, Yankai Yang, G. Edward Suh +2
Large Language Models (LLMs) have enabled the development of powerful agentic systems capable of automating complex workflows across various fields. However, these systems are high…