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cs.CR2026
Your Agentic LLMs Secretly Encode Indirect Prompt-Injection Exposure in Hidden States
Jianshuo Dong, Yiming Liu, Maosen Zhang +6
Agentic LLMs are vulnerable to indirect prompt injection (IPI) attacks, e.g., malicious side-tasks hidden in external tool results. While many efforts have sought to address this t…
cs.CR2026
The Model's Tell: Measuring Context-Leakage Attack Signals with Behavior Gauges
Maosen Zhang, Jianshuo Dong, Boting Lu +5
LLMs increasingly rely on external contexts, such as pre-defined system prompts or retrieved documents, to improve generation quality. However, processing these contexts alongside…
cs.CR2026
LeakDojo: Decoding the Leakage Threats of RAG Systems
Maosen Zhang, Jianshuo Dong, Boting Lu +4
Retrieval-Augmented Generation (RAG) enables large language models (LLMs) to leverage external knowledge, but also exposes valuable RAG databases to leakage attacks. As RAG systems…