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cs.CR2025
Understanding Gaps in LLM Pipelines Towards Scalable Fuzzing Harness Generation: An Empirical Study and Enhancement
Kang Yang, Yunhang Zhang, Zichuan Li +3
Large language model (LLM)-based techniques have achieved notable progress in fuzz harness generation. However, applying them to arbitrary functions \textit{at scale} remains diffi…
cs.CR2025★ 1 cited
Blackbox Dataset Inference for LLM
Ruikai Zhou, Kang Yang, Xun Chen +3
Today, the training of large language models (LLMs) can involve personally identifiable information and copyrighted material, incurring dataset misuse. To mitigate the problem of d…
cs.CR2025
Alleviating the Fear of Losing Alignment in LLM Fine-tuning
Kang Yang, Guanhong Tao, Xun Chen +1
Large language models (LLMs) have demonstrated revolutionary capabilities in understanding complex contexts and performing a wide range of tasks. However, LLMs can also answer ques…