3 citations · 6 across the 14 of their papers we have counts for
5 papers · 1 filter
How Do LLM Agents Actually Get the Flag? Trace-Level Provenance for Agentic Offensive Security Evaluation
Kimberly Milner, Minghao Shao, Nanda Rani +8
Capture-the-Flag (CTF) benchmarks are widely used to assess the offensive security capabilities of autonomous language-model agents. Evaluations rely on shallow binary judgments or…
Sandbox-Enabled Digital Twin for Cyber-Physical Systems
Meet Udeshi, Md Raz, Prashanth Krishnamurthy +2
Firmware/software in cyber-physical system (CPS) embedded devices/controllers can have vulnerabilities stemming from multiple sources such as weak security practices, outdated libr…
AI In Cybersecurity Education -- Scalable Agentic CTF Design Principles and Educational Outcomes
Haoran Xi, Minghao Shao, Kimberly Milner +11
Large language models are rapidly changing how learners acquire and demonstrate cybersecurity skills. However, when human--AI collaboration is allowed, educators still lack validat…
Safeguarding LLMs Against Misuse and AI-Driven Malware Using Steganographic Canaries
Md Raz, Venkata Sai Charan Putrevu, Meet Udeshi +3
AI-powered malware increasingly exploits cloud-hosted generative-AI services and large language models (LLMs) as analysis engines for reconnaissance and code generation. Simultaneo…
CTFExplorer: Evaluating LLM Offensive Agents Through Multi-Target Web CTF Benchmarking
Nanda Rani, Kimberly Milner, Minghao Shao +9
Existing benchmarks for LLM-based offensive security agents use isolated, single-target setups with a known vulnerable service and fixed objective. They measure exploitation effect…