2 citations · 5 across the 12 of their papers we have counts for
7 papers · 1 filter
SelfOp: An Optimization Algorithm for Self-Improving Security Agents
Saad Ullah, Yigitcan Kaya, Christopher Kruegel +2
LLM agents are increasingly used for security tasks: vulnerability discovery, exploit reproduction, and patch generation. Improving them at the model level demands expert demonstra…
A SoK for SoCs: Reading the TI Leaves on AI for Cyber Threat Intelligence Generation and Sharing
Saastha Vasan, Hadjer Benkraouda, Jizhou Chen +8
Cyber Threat Intelligence (CTI) is essential for defending mission-critical infrastructure, yet the process of transforming raw attack evidence into shareable CTI remains fragmente…
From Documentation to Zero-day Vulnerabilities: LLM-Driven Fuzzing of JavaScript Engines in PDF Readers
Suyue Guo, Stijn Pletinckx, Tianle Yu +5
Existing fuzzers for PDF readers rely on simple test cases that involve only individual API calls, leading to limited coverage and potentially missing vulnerabilities that require…
SeedSmith: LLM-Driven Seed Synthesis for Directed Fuzzing
Junmin Zhu, Siyu Liu, Jie Hu +7
Directed fuzzing steers fuzzers toward user-defined sink functions to identify vulnerabilities, but it frequently fails to trigger crashes even after long campaigns. We identify tw…
MalwarePT: A Binary-Level Foundation Model for Malware Analysis
Saastha Vasan, Yuzhou Nie, Kaie Chen +6
Automated malware analysis increasingly relies on machine learning, yet most existing methods remain task-specific and depend on handcrafted features or narrowly scoped models. Rec…
DevOps-Gym: Benchmarking AI Agents in Software DevOps Cycle
Yuheng Tang, Kaijie Zhu, Bonan Ruan +14
Even though demonstrating extraordinary capabilities in code generation and software issue resolving, AI agents' capabilities in the full software DevOps cycle are still unknown. D…
Multi-Agent Taint Specification Extraction for Vulnerability Detection
Jonah Ghebremichael, Saastha Vasan, Saad Ullah +6
Static Application Security Testing (SAST) tools using taint analysis are widely viewed as providing higher-quality vulnerability detection results compared to traditional pattern-…