1 citations · 1 across the 10 of their papers we have counts for
4 papers · 1 filter
PhantomCall: Evading ML Malware Detectors via Function Call Graph Perturbation
Md Ajwad Akil, Adrian Shuai Li, Imtiaz Karim +3
Prior adversarial attacks on Windows PE malware detectors target raw bytes, PE headers, or intra-function control-flow graphs, leaving the function call graph (FCG) unexplored as a…
ChronoMem: Version Control and Semantic Rollback for Large Language Model Agent Memory
Yongye Su, Wujiang Xu, Chaoji Zuo +1
LLM agents increasingly rely on long-term memory to support multi-session interaction and personalization. However, existing agent memory systems are designed around forward-only e…
Information Theoretic Adversarial Training of Large Language Models
Yiwei Zhang, Jeremiah Birrell, Reza Ebrahimi +3
Large language models (LLMs) remain vulnerable to adversarial prompting despite advances in alignment and safety, often exhibiting harmful behaviors under novel attack strategies.…
Can Drift-Adaptive Malware Detectors Be Made Robust? Attacks and Defenses Under White-Box and Black-Box Threats
Adrian Shuai Li, Md Ajwad Akil, Elisa Bertino
Concept drift and adversarial evasion are two major challenges for deploying machine learning-based malware detectors. While both have been studied separately, their combination, t…