17 papers
External Data Extraction Attacks against Retrieval-Augmented Large Language Models
Yu He, Yifei Chen, Yiming Li +5
In recent years, RAG has emerged as a key paradigm for enhancing large language models (LLMs). By integrating externally retrieved information, RAG alleviates issues like outdated…
MIRAGE: Misleading Retrieval-Augmented Generation via Black-box and Query-agnostic Poisoning Attacks
Tailun Chen, Yu He, Yan Wang +9
Retrieval-Augmented Generation (RAG) systems enhance LLMs with external knowledge but introduce a critical attack surface: corpus poisoning. While recent studies have demonstrated…
AttriGuard: Defeating Indirect Prompt Injection in LLM Agents via Causal Attribution of Tool Invocations
Yu He, Haozhe Zhu, Yiming Li +4
LLM agents are highly vulnerable to Indirect Prompt Injection (IPI), where adversaries embed malicious directives in untrusted tool outputs to hijack execution. Most existing defen…
FIT-Print: Towards False-claim-resistant Model Ownership Verification via Targeted Fingerprint
Shuo Shao, Haozhe Zhu, Yiming Li +3
Model fingerprinting has emerged as a crucial mechanism for safeguarding the intellectual property of open-source models, offering a non-intrusive approach that requires no modific…
PromptCOS: Towards Content-only System Prompt Copyright Auditing for LLMs
Yuchen Yang, Yiming Li, Hongwei Yao +6
System prompts are critical for shaping the behavior and output quality of large language model (LLM)-based applications, driving substantial investment in optimizing high-quality…
Retrofit: Continual Learning with Controlled Forgetting for Binary Security Detection and Analysis
Yiling He, Junchi Lei, Hongyu She +5
Binary security has increasingly relied on deep learning to reason about malware behavior and program semantics. However, the performance often degrades as threat landscapes evolve…