8 citations · 9 across the 6 of their papers we have counts for
6 papers
Compiling Activation Steering into Weights via Null-Space Constraints for Stealthy Backdoors
Rui Yin, Tianxu Han, Naen Xu +8
Safety-aligned large language models (LLMs) are increasingly deployed in real-world pipelines, yet this deployment also enlarges the supply-chain attack surface: adversaries can di…
On the Security Risks of ML-based Malware Detection Systems: A Survey
Ping He, Yuhao Mao, Changjiang Li +3
Malware presents a persistent threat to user privacy and data integrity. To combat this, machine learning-based (ML-based) malware detection (MD) systems have been developed. Howev…
Defending against Adversarial Malware Attacks on ML-based Android Malware Detection Systems
Ping He, Lorenzo Cavallaro, Shouling Ji
Android malware presents a persistent threat to users' privacy and data integrity. To combat this, researchers have proposed machine learning-based (ML-based) Android malware detec…
Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats in LLM-Based Agents
Yuyou Gan, Yong Yang, Zhe Ma +10
With the continuous development of large language models (LLMs), transformer-based models have made groundbreaking advances in numerous natural language processing (NLP) tasks, lea…
Static Semantics Reconstruction for Enhancing JavaScript-WebAssembly Multilingual Malware Detection
Yifan Xia, Ping He, Xuhong Zhang +3
The emergence of WebAssembly allows attackers to hide the malicious functionalities of JavaScript malware in cross-language interoperations, termed JavaScript-WebAssembly multiling…
Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting
Ping He, Yifan Xia, Xuhong Zhang +1
The widespread adoption of the Android operating system has made malicious Android applications an appealing target for attackers. Machine learning-based (ML-based) Android malware…