3 citations · 5 across the 28 of their papers we have counts for
10 papers · 1 filter
Class-feature Watermark: A Resilient Black-box Watermark Against Model Extraction Attacks
Yaxin Xiao, Qingqing Ye, Zi Liang +4
Machine learning models constitute valuable intellectual property, yet remain vulnerable to model extraction attacks (MEA), where adversaries replicate their functionality through…
Virus Infection Attack on LLMs: Your Poisoning Can Spread "VIA" Synthetic Data
Zi Liang, Qingqing Ye, Xuan Liu +3
Synthetic data refers to artificial samples generated by models. While it has been validated to significantly enhance the performance of large language models (LLMs) during trainin…
Toward Efficient Inference Attacks: Shadow Model Sharing via Mixture-of-Experts
Li Bai, Qingqing Ye, Xinwei Zhang +4
Machine learning models are often vulnerable to inference attacks that expose sensitive information from their training data. Shadow model technique is commonly employed in such at…
Decision Potential Surface: A Theoretical and Practical Approximation of Large Language Model Decision Boundary
Zi Liang, Zhiyao Wu, Haoyang Shang +5
Decision boundary, the subspace of inputs where a machine learning model assigns equal classification probabilities to two classes, is pivotal in revealing core model properties an…
The Matthew Effect of AI Programming Assistants: A Hidden Bias in Software Evolution
Fei Gu, Zi Liang, Jiahao MA +1
AI-assisted programming is rapidly reshaping software development, with large language models (LLMs) enabling new paradigms such as vibe coding and agentic coding. While prior work…
Reminiscence Attack on Residuals: Exploiting Approximate Machine Unlearning for Privacy
Yaxin Xiao, Qingqing Ye, Li Hu +5
Machine unlearning enables the removal of specific data from ML models to uphold the right to be forgotten. While approximate unlearning algorithms offer efficient alternatives to…
How Much Do Large Language Model Cheat on Evaluation? Benchmarking Overestimation under the One-Time-Pad-Based Framework
Zi Liang, Liantong Yu, Shiyu Zhang +2
Overestimation in evaluating large language models (LLMs) has become an increasing concern. Due to the contamination of public benchmarks or imbalanced model training, LLMs may ach…
United Minds or Isolated Agents? Exploring Coordination of LLMs under Cognitive Load Theory
HaoYang Shang, Xuan Liu, Zi Liang +3
Large Language Models (LLMs) exhibit a notable performance ceiling on complex, multi-faceted tasks. As practitioners increasingly rely on heavy context engineering -- curating intr…
Does Low Rank Adaptation Lead to Lower Robustness against Training-Time Attacks?
Zi Liang, Haibo Hu, Qingqing Ye +2
Low rank adaptation (LoRA) has emerged as a prominent technique for fine-tuning large language models (LLMs) thanks to its superb efficiency gains over previous methods. While exte…
How Vital is the Jurisprudential Relevance: Law Article Intervened Legal Case Retrieval and Matching
Nuo Xu, Pinghui Wang, Zi Liang +2
Legal case retrieval (LCR) aims to automatically scour for comparable legal cases based on a given query, which is crucial for offering relevant precedents to support the judgment…