12 citations · 28 across the 35 of their papers we have counts for
14 papers · 1 filter
The Safeguard Worked. Is the LLM System Safer?
Pingyu Wu, Weiming Zhang, Nenghai Yu
Safeguards in deployed LLM services are evaluated by refusal, attack success, and policy violation rates. Those rates characterize how a control performed on the requests it was te…
Breadcrumbing Search Agents
Xuebin Li, Hanqing Zhao, Siyuan Liang +4
LLM-based search agents are widely used for information-seeking tasks, but their reliance on external tool returns introduces a critical security risk: web content retrieved during…
Safeguards Based on Copyable Context Cannot Provide Reliable Safety for LLMs
Pingyu Wu, Lingyao Zhu, Weiming Zhang +1
Large language model safeguards decide whether to answer before seeing how an answer will be used. This creates a basic problem for dual-use tasks: the same answer can help an auth…
PSRT: Accelerating LRM-based Guard Models via Prefilled Safe Reasoning Traces
Jiawei Zhao, Yuang Qi, Weiming Zhang +2
Large Reasoning Models (LRMs) have demonstrated remarkable performance on tasks such as mathematics and code generation. Motivated by these strengths, recent work has empirically d…
Beyond the Edge of Function: Unraveling the Patterns of Type Recovery in Binary Code
Gangyang Li, Xiuwei Shang, Shaoyin Cheng +5
Type recovery is a crucial step in binary code analysis, holding significant importance for reverse engineering and various security applications. Existing works typically simply t…
AutoPT: How Far Are We from the End2End Automated Web Penetration Testing?
Benlong Wu, Guoqiang Chen, Kejiang Chen +5
Penetration testing is essential to ensure Web security, which can detect and fix vulnerabilities in advance, and prevent data leakage and serious consequences. The powerful infere…
SQL Injection Jailbreak: A Structural Disaster of Large Language Models
Jiawei Zhao, Kejiang Chen, Weiming Zhang +1
Large Language Models (LLMs) are susceptible to jailbreak attacks that can induce them to generate harmful content. Previous jailbreak methods primarily exploited the internal prop…
FoC: Figure out the Cryptographic Functions in Stripped Binaries with LLMs
Xiuwei Shang, Guoqiang Chen, Shaoyin Cheng +4
Analyzing the behavior of cryptographic functions in stripped binaries is a challenging but essential task. Cryptographic algorithms exhibit greater logical complexity compared to…
Provably Secure Disambiguating Neural Linguistic Steganography
Yuang Qi, Kejiang Chen, Kai Zeng +2
Recent research in provably secure neural linguistic steganography has overlooked a crucial aspect: the sender must detokenize stegotexts to avoid raising suspicion from the eavesd…
Model X-ray:Detecting Backdoored Models via Decision Boundary
Yanghao Su, Jie Zhang, Ting Xu +3
Backdoor attacks pose a significant security vulnerability for deep neural networks (DNNs), enabling them to operate normally on clean inputs but manipulate predictions when specif…
Silent Guardian: Protecting Text from Malicious Exploitation by Large Language Models
Jiawei Zhao, Kejiang Chen, Xiaojian Yuan +3
The rapid development of large language models (LLMs) has yielded impressive success in various downstream tasks. However, the vast potential and remarkable capabilities of LLMs al…
Performance-lossless Black-box Model Watermarking
Na Zhao, Kejiang Chen, Weiming Zhang +1
With the development of deep learning, high-value and high-cost models have become valuable assets, and related intellectual property protection technologies have become a hot topi…
Turning Your Strength into Watermark: Watermarking Large Language Model via Knowledge Injection
Shuai Li, Kejiang Chen, Kunsheng Tang +4
Large language models (LLMs) have demonstrated outstanding performance, making them valuable digital assets with significant commercial potential. Unfortunately, the LLM and its AP…
Segue: Side-information Guided Generative Unlearnable Examples for Facial Privacy Protection in Real World
Zhiling Zhang, Jie Zhang, Kui Zhang +3
The widespread use of face recognition technology has given rise to privacy concerns, as many individuals are worried about the collection and utilization of their facial data. To…