27 citations · 85 across the 22 of their papers we have counts for
24 papers
MATRIX: Multi-Layer Code Watermarking via Dual-Channel Constrained Parity-Check Encoding
Yuqing Nie, Chong Wang, Guosheng Xu +4
Code Large Language Models (Code LLMs) have revolutionized software development but raised critical concerns regarding code provenance, copyright protection, and security. Existing…
Embedding Poisoning: Bypassing Safety Alignment via Embedding Semantic Shift
Shuai Yuan, Zhibo Zhang, Yuxi Li +2
The widespread distribution of Large Language Models (LLMs) through public platforms like Hugging Face introduces significant security challenges. While these platforms perform bas…
Boosting Pointer Analysis With LLM-Enhanced Allocation Function Detection
Baijun Cheng, Kailong Wang, Ling Shi +5
Pointer analysis is foundational for many static analysis tasks, yet its effectiveness is often hindered by imprecise modeling of heap allocations, particularly in C/C++ programs w…
Enhancing Semantic Understanding in Pointer Analysis using Large Language Models
Baijun Cheng, Kailong Wang, Ling Shi +4
Pointer analysis has been studied for over four decades. However, existing frameworks continue to suffer from the propagation of incorrect facts. A major limitation stems from thei…
Circumventing Safety Alignment in Large Language Models Through Embedding Space Toxicity Attenuation
Zhibo Zhang, Yuxi Li, Kailong Wang +3
Large Language Models (LLMs) have achieved remarkable success across domains such as healthcare, education, and cybersecurity. However, this openness also introduces significant se…
Exposing the Ghost in the Transformer: Abnormal Detection for Large Language Models via Hidden State Forensics
Shide Zhou, Kailong Wang, Ling Shi +1
The widespread adoption of Large Language Models (LLMs) in critical applications has introduced severe reliability and security risks, as LLMs remain vulnerable to notorious threat…
Model-Editing-Based Jailbreak against Safety-aligned Large Language Models
Yuxi Li, Zhibo Zhang, Kailong Wang +2
Large Language Models (LLMs) have transformed numerous fields by enabling advanced natural language interactions but remain susceptible to critical vulnerabilities, particularly ja…
Efficient and Effective Universal Adversarial Attack against Vision-Language Pre-training Models
Fan Yang, Yihao Huang, Kailong Wang +4
Vision-language pre-training (VLP) models, trained on large-scale image-text pairs, have become widely used across a variety of downstream vision-and-language (V+L) tasks. This wid…
Decoding Secret Memorization in Code LLMs Through Token-Level Characterization
Yuqing Nie, Chong Wang, Kailong Wang +3
Code Large Language Models (LLMs) have demonstrated remarkable capabilities in generating, understanding, and manipulating programming code. However, their training process inadver…
GlitchProber: Advancing Effective Detection and Mitigation of Glitch Tokens in Large Language Models
Zhibo Zhang, Wuxia Bai, Yuxi Li +6
Large language models (LLMs) have achieved unprecedented success in the field of natural language processing. However, the black-box nature of their internal mechanisms has brought…
Models Are Codes: Towards Measuring Malicious Code Poisoning Attacks on Pre-trained Model Hubs
Jian Zhao, Shenao Wang, Yanjie Zhao +6
The proliferation of pre-trained models (PTMs) and datasets has led to the emergence of centralized model hubs like Hugging Face, which facilitate collaborative development and reu…
Towards Robust Detection of Open Source Software Supply Chain Poisoning Attacks in Industry Environments
Xinyi Zheng, Chen Wei, Shenao Wang +5
The exponential growth of open-source package ecosystems, particularly NPM and PyPI, has led to an alarming increase in software supply chain poisoning attacks. Existing static ana…
Understanding the Effectiveness of Coverage Criteria for Large Language Models: A Special Angle from Jailbreak Attacks
Shide Zhou, Tianlin Li, Kailong Wang +4
Large language models (LLMs) have revolutionized artificial intelligence, but their increasing deployment across critical domains has raised concerns about their abnormal behaviors…
Semantic-Enhanced Indirect Call Analysis with Large Language Models
Baijun Cheng, Cen Zhang, Kailong Wang +6
In contemporary software development, the widespread use of indirect calls to achieve dynamic features poses challenges in constructing precise control flow graphs (CFGs), which fu…
NeuSemSlice: Towards Effective DNN Model Maintenance via Neuron-level Semantic Slicing
Shide Zhou, Tianlin Li, Yihao Huang +4
Deep Neural networks (DNNs), extensively applied across diverse disciplines, are characterized by their integrated and monolithic architectures, setting them apart from conventiona…
WALLETRADAR: Towards Automating the Detection of Vulnerabilities in Browser-based Cryptocurrency Wallets
Pengcheng Xia, Yanhui Guo, Zhaowen Lin +8
Cryptocurrency wallets, acting as fundamental infrastructure to the blockchain ecosystem, have seen significant user growth, particularly among browser-based wallets (i.e., browser…
Large Language Models for Cyber Security: A Systematic Literature Review
Hanxiang Xu, Shenao Wang, Ningke Li +6
The rapid advancement of Large Language Models (LLMs) has opened up new opportunities for leveraging artificial intelligence in a variety of application domains, including cybersec…
Drowzee: Metamorphic Testing for Fact-Conflicting Hallucination Detection in Large Language Models
Ningke Li, Yuekang Li, Yi Liu +3
Large language models (LLMs) have transformed the landscape of language processing, yet struggle with significant challenges in terms of security, privacy, and the generation of se…
Beyond Fidelity: Explaining Vulnerability Localization of Learning-based Detectors
Baijun Cheng, Shengming Zhao, Kailong Wang +6
Vulnerability detectors based on deep learning (DL) models have proven their effectiveness in recent years. However, the shroud of opacity surrounding the decision-making process o…
MalModel: Hiding Malicious Payload in Mobile Deep Learning Models with Black-box Backdoor Attack
Jiayi Hua, Kailong Wang, Meizhen Wang +3
Mobile malware has become one of the most critical security threats in the era of ubiquitous mobile computing. Despite the intensive efforts from security experts to counteract it,…
The Devil Behind the Mirror: Tracking the Campaigns of Cryptocurrency Abuses on the Dark Web
Pengcheng Xia, Zhou Yu, Kailong Wang +6
The dark web has emerged as the state-of-the-art solution for enhanced anonymity. Just like a double-edged sword, it also inadvertently becomes the safety net and breeding ground f…
MiniScope: Automated UI Exploration and Privacy Inconsistency Detection of MiniApps via Two-phase Iterative Hybrid Analysis
Shenao Wang, Yuekang Li, Kailong Wang +4
The advent of MiniApps, operating within larger SuperApps, has revolutionized user experiences by offering a wide range of services without the need for individual app downloads. H…
SliceLocator: Locating Vulnerable Statements with Graph-based Detectors
Baijun Cheng, Kailong Wang, Cuiyun Gao +5
Vulnerability detection is a crucial component in the software development lifecycle. Existing vulnerability detectors, especially those based on deep learning (DL) models, have ac…
Digger: Detecting Copyright Content Mis-usage in Large Language Model Training
Haodong Li, Gelei Deng, Yi Liu +7
Pre-training, which utilizes extensive and varied datasets, is a critical factor in the success of Large Language Models (LLMs) across numerous applications. However, the detailed…