3 citations · 5 across the 10 of their papers we have counts for
6 papers · 1 filter
Not All Tokens Are Equal: Region-Aware Consistency Repair of Backdoors in MLLMs
Jiali Wei, Ming Fan, Mingkun Zhang +6
MLLMs are increasingly deployed in user-facing applications, yet they inherit backdoor risks from the pipelines used to construct them: triggers may reside in images, texts, or bot…
ClawGuard: A Runtime Security Framework for Tool-Augmented LLM Agents Against Indirect Prompt Injection
Wei Zhao, Zhe Li, Peixin Zhang +1
Tool-augmented Large Language Model (LLM) agents have demonstrated impressive capabilities in automating complex, multi-step real-world tasks, yet remain vulnerable to indirect pro…
The Salami Slicing Threat: Exploiting Cumulative Risks in LLM Systems
Yihao Zhang, Kai Wang, Jiangrong Wu +7
Large Language Models (LLMs) face prominent security risks from jailbreaking, a practice that manipulates models to bypass built-in security constraints and generate unethical or u…
Rounding-Guided Backdoor Injection in Deep Learning Model Quantization
Xiangxiang Chen, Peixin Zhang, Jun Sun +2
Model quantization is a popular technique for deploying deep learning models on resource-constrained environments. However, it may also introduce previously overlooked security ris…
Efficient and Universal Watermarking for LLM-Generated Code Detection
Boquan Li, Zirui Fu, Mengdi Zhang +3
Large language models (LLMs) have significantly enhanced the usability of AI-generated code, providing effective assistance to programmers. This advancement also raises ethical and…
Exploiting Machine Unlearning for Backdoor Attacks in Deep Learning System
Peixin Zhang, Jun Sun, Mingtian Tan +1
In recent years, the security issues of artificial intelligence have become increasingly prominent due to the rapid development of deep learning research and applications. Backdoor…