5 citations · 13 across the 20 of their papers we have counts for
18 papers · 1 filter
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…
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…
Black-Box Guardrail Reverse-engineering Attack
Hongwei Yao, Yun Xia, Shuo Shao +3
Large language models (LLMs) increasingly employ guardrails to enforce ethical, legal, and application-specific constraints on their outputs. While effective at mitigating harmful…
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 By integrating externally retrieved information, RAG alleviates issues like outdated…
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…
MAJIC: Markovian Adaptive Jailbreaking via Iterative Composition of Diverse Innovative Strategies
Weiwei Qi, Shuo Shao, Wei Gu +4
Large Language Models (LLMs) have exhibited remarkable capabilities but remain vulnerable to jailbreaking attacks, which can elicit harmful content from the models by manipulating…
SoK: Large Language Model Copyright Auditing via Fingerprinting
Shuo Shao, Yiming Li, Yu He +4
The broad capabilities and substantial resources required to train Large Language Models (LLMs) make them valuable intellectual property, yet they remain vulnerable to copyright in…
Shadow in the Cache: Unveiling and Mitigating Privacy Risks of KV-cache in LLM Inference
Zhifan Luo, Shuo Shao, Su Zhang +5
The Key-Value (KV) cache, which stores intermediate attention computations (Key and Value pairs) to avoid redundant calculations, is a fundamental mechanism for accelerating Large…
DATABench: Evaluating Dataset Auditing in Deep Learning from an Adversarial Perspective
Shuo Shao, Yiming Li, Mengren Zheng +7
The widespread application of Deep Learning across diverse domains hinges critically on the quality and composition of training datasets. However, the common lack of disclosure reg…
SmartGuard: Leveraging Large Language Models for Network Attack Detection through Audit Log Analysis and Summarization
Hao Zhang, Shuo Shao, Song Li +3
End-point monitoring solutions are widely deployed in today's enterprise environments to support advanced attack detection and investigation. These monitors continuously record sys…
CBW: Towards Dataset Ownership Verification for Speaker Verification via Clustering-based Backdoor Watermarking
Yiming Li, Kaiying Yan, Shuo Shao +5
Speaker verification models are trained on large-scale public datasets whose licenses usually prohibit unauthorized commercial use, yet such infringement is difficult to detect or…
REFINE: Inversion-Free Backdoor Defense via Model Reprogramming
Yukun Chen, Shuo Shao, Enhao Huang +4
Backdoor attacks on deep neural networks (DNNs) have emerged as a significant security threat, allowing adversaries to implant hidden malicious behaviors during the model training…
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…
SoK: On the Role and Future of AIGC Watermarking in the Era of Gen-AI
Kui Ren, Ziqi Yang, Li Lu +6
The rapid advancement of AI technology, particularly in generating AI-generated content (AIGC), has transformed numerous fields, e.g., art video generation, but also brings new ris…
PointNCBW: Towards Dataset Ownership Verification for Point Clouds via Negative Clean-label Backdoor Watermark
Cheng Wei, Yang Wang, Kuofeng Gao +4
Recently, point clouds have been widely used in computer vision, whereas their collection is time-consuming and expensive. As such, point cloud datasets are the valuable intellectu…
Explanation as a Watermark: Towards Harmless and Multi-bit Model Ownership Verification via Watermarking Feature Attribution
Shuo Shao, Yiming Li, Hongwei Yao +3
Ownership verification is currently the most critical and widely adopted post-hoc method to safeguard model copyright. In general, model owners exploit it to identify whether a giv…
FedTracker: Furnishing Ownership Verification and Traceability for Federated Learning Model
Shuo Shao, Wenyuan Yang, Hanlin Gu +4
Federated learning (FL) is a distributed machine learning paradigm allowing multiple clients to collaboratively train a global model without sharing their local data. However, FL e…
Watermarking in Secure Federated Learning: A Verification Framework Based on Client-Side Backdooring
Wenyuan Yang, Shuo Shao, Yue Yang +5
Federated learning (FL) allows multiple participants to collaboratively build deep learning (DL) models without directly sharing data. Consequently, the issue of copyright protecti…