13 citations · 23 across the 21 of their papers we have counts for
10 papers · 1 filter
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…
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…
Ransomware 3.0: Self-Composing and LLM-Orchestrated
Md Raz, Meet Udeshi, P. V. Sai Charan +3
Using automated reasoning, code synthesis, and contextual decision-making, we introduce a new threat that exploits large language models (LLMs) to autonomously plan, adapt, and exe…
SCAMPER -- Synchrophasor Covert chAnnel for Malicious and Protective ERrands
Prashanth Krishnamurthy, Ramesh Karri, Farshad Khorrami
We note that constituent fields (notably the fraction-of-seconds timestamp field) in the data payload structure of the synchrophasor communication protocol (IEEE C37.118 standard)…
SaMOSA: Sandbox for Malware Orchestration and Side-Channel Analysis
Meet Udeshi, Venkata Sai Charan Putrevu, Prashanth Krishnamurthy +2
Cyber-attacks on operational technology (OT) and cyber-physical systems (CPS) have increased tremendously in recent years with the proliferation of malware targeting Linux-based em…
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…