1 citations · 1 across the 11 of their papers we have counts for
12 papers
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…
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…
Binary Diff Summarization using Large Language Models
Meet Udeshi, Venkata Sai Charan Putrevu, Prashanth Krishnamurthy +4
Security of software supply chains is necessary to ensure that software updates do not contain maliciously injected code or introduce vulnerabilities that may compromise the integr…
An Orchestrated Sandbox for Side-Channel Analysis of Cyber-Physical Systems Malware
Meet Udeshi, Venkata Sai Charan Putrevu, Prashanth Krishnamurthy +2
Embedded devices powering cyber-physical systems (CPS) are increasingly internet-exposed, placing critical infrastructure under active threat. Dynamic analysis of embedded malware…
Towards Effective Offensive Security LLM Agents: Hyperparameter Tuning, LLM as a Judge, and a Lightweight CTF Benchmark
Minghao Shao, Nanda Rani, Kimberly Milner +9
Recent advances in LLM agentic systems have improved the automation of offensive security tasks, particularly for Capture the Flag (CTF) challenges. We systematically investigate t…
CRAKEN: Cybersecurity LLM Agent with Knowledge-Based Execution
Minghao Shao, Haoran Xi, Nanda Rani +9
Large Language Model (LLM) agents can automate cybersecurity tasks and can adapt to the evolving cybersecurity landscape without re-engineering. While LLM agents have demonstrated…
D-CIPHER: Dynamic Collaborative Intelligent Multi-Agent System with Planner and Heterogeneous Executors for Offensive Security
Meet Udeshi, Minghao Shao, Haoran Xi +9
Large Language Models (LLMs) have been used in cybersecurity such as autonomous security analysis or penetration testing. Capture the Flag (CTF) challenges serve as benchmarks to a…
SHIELD: A Host-Independent Framework for Ransomware Detection using Deep Filesystem Features
Md Raz, Venkata Sai Charan Putrevu, Prashanth Krishnamurthy +2
Ransomware's escalating sophistication necessitates tamper-resistant, off-host detection solutions that capture deep disk activity beyond the reach of a compromised operating syste…
PrefixLLM: LLM-aided Prefix Circuit Design
Weihua Xiao, Venkata Sai Charan Putrevu, Raghu Vamshi Hemadri +2
Prefix circuits are fundamental components in digital adders, widely used in digital systems due to their efficiency in calculating carry signals. Synthesizing prefix circuits with…
A Comprehensive Analysis of Machine Learning Based File Trap Selection Methods to Detect Crypto Ransomware
Mohan Anand Putrevu, Hrushikesh Chunduri, Venkata Sai Charan Putrevu +1
The use of multi-threading and file prioritization methods has accelerated the speed at which ransomware encrypts files. To minimize file loss during the ransomware attack, detecti…