4 citations · 6 across the 12 of their papers we have counts for
8 papers · 1 filter
Antaeus: Hunting Repository-Level Logic Vulnerabilities via Context-Grounded LLM Reasoning
Michele Armillotta, Nicolò Romandini, Rebecca Montanari +1
LLM-based vulnerability detectors have shown promising results in identifying memory-safety bugs and vulnerability classes whose violations can often be expressed through establish…
Poster: Rethinking Security in LLM Code Generation through Real-World Risk Scenarios
Lixun Ma, Ruolong Ma, Bei Wang +4
Large Language Models (LLMs) are widely used for code generation, yet their security behavior in realistic development workflows remains underexplored. Existing benchmarks often re…
Malaika: Understanding Malware through Tri-Grounded Agentic Reasoning
Xingzhi Qian, Xinran Zheng, Yiling He +1
Recent LLM-based systems have shown promising capabilities for security-focused code analysis. Malware understanding, however, poses a distinct challenge: analysts must reconstruct…
Demystifying the Mythos or Disrupting Bugonomics? From Zero-Day Asymmetry to Defender Remediation Throughput
Alfredo Pesoli, Herman Errico, Lorenzo Cavallaro
Recent demonstrations of large language models producing candidate and confirmed vulnerabilities in production software have renewed the narrative that AI will reshape offensive an…
Is "Knowing It's Malicious Enough?" Evaluating LLMs for Fine-Grained Malware Behavior Auditing
Xinran Zheng, Xingzhi Qian, Yiling He +2
Automated malware classifiers achieve strong detection performance, but auditing requires more than flagging a sample: analysts must explain malicious behaviors and justify them wi…
On Benchmarking Code LLMs for Android Malware Analysis
Yiling He, Hongyu She, Xingzhi Qian +4
Large Language Models (LLMs) have demonstrated strong capabilities in various code intelligence tasks. However, their effectiveness for Android malware analysis remains underexplor…