9 citations · 12 across the 25 of their papers we have counts for
7 papers · 1 filter
Robustness-Aware Evaluation and Enhancement of Mutation-Based Fuzzing for Bug Discovery
Zirui Liu, Mengfan Xu, Juan Zhai +2
Mutation-based fuzzing is widely used to discover software vulnerabilities, but its randomness complicates rigorous evaluation and reliable bug detection. Prior work measures this…
CAROL: Context-Aware Online Learning for Fuzzer Scheduling
Zirui Liu, Mengfan Xu, Juan Zhai +2
Ensemble fuzzing runs multiple fuzzers on a target while a scheduler allocates CPU time among them. Existing schedulers base these decisions on compact summaries of past performanc…
Source or It Didn't Happen: A Multi-Agent Framework for Citation Hallucination Detection
Mingzhe Li, Zhiqiang Lin, Shiqing Ma
Large language models are increasingly used in scientific writing, yet they can fabricate citation-shaped references that appear plausible but fail bibliographic verification. Exis…
Train in Vain: Functionality-Preserving Poisoning to Prevent Unauthorized Use of Code Datasets
Yuan Xiao, Jiaming Wang, Yuchen Chen +8
The widespread availability of large-scale code datasets has accelerated the development of code large language models (CodeLLMs), raising concerns about unauthorized dataset usage…
Weaponizing the Commons: A Taxonomy and Detection Framework of Abuse on GitHub
Yuli Cheng, Xiaoyu Zhang, Jiongchi Yu +3
GitHub plays a critical role in modern software supply chains, making its security an important research concern. Existing studies have primarily focused on CI/CD automation, colla…
Domain-Specialized Tree of Thought through Plug-and-Play Predictors
Xuanqi Gao, Haoyu Wang, Jun Sun +2
While Large Language Models (LLMs) have advanced complex reasoning, prominent methods like the Tree of Thoughts (ToT) framework face a critical trade-off between exploration depth…
False Friends in the Shell: Unveiling the Emoticon Semantic Confusion in Large Language Models
Weipeng Jiang, Xiaoyu Zhang, Juan Zhai +3
Emoticons are widely used in digital communication to convey affective intent, yet their safety implications for Large Language Models (LLMs) remain largely unexplored. In this pap…