3 papers
cs.SE2025
Fuzzing: Randomness? Reasoning! Efficient Directed Fuzzing via Large Language Models
Xiaotao Feng, Xiaogang Zhu, Kun Hu +4
Fuzzing is highly effective in detecting bugs due to the key contribution of randomness. However, randomness significantly reduces the efficiency of fuzzing, causing it to cost day…
cs.CR2025
Efficient Jailbreaking of Large Models by Freeze Training: Lower Layers Exhibit Greater Sensitivity to Harmful Content
Hongyuan Shen, Min Zheng, Jincheng Wang +1
With the widespread application of Large Language Models across various domains, their security issues have increasingly garnered significant attention from both academic and indus…
cs.CR2025
Modern DDoS Threats and Countermeasures: Insights into Emerging Attacks and Detection Strategies
Jincheng Wang, Le Yu, John C. S. Lui +1
Distributed Denial of Service (DDoS) attacks persist as significant threats to online services and infrastructure, evolving rapidly in sophistication and eluding traditional detect…