From the 1 of 8 linked papers with an AI index.
8 papers
ChronoMem: Version Control and Semantic Rollback for Large Language Model Agent Memory
Yongye Su, Wujiang Xu, Chaoji Zuo +1
ChronoMem adds a semantic version‑control layer to large language model agents, allowing them to snapshot, browse, and roll back their long‑term memory using natural‑language reque…
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…
LFreeDA: Label-Free Drift Adaptation for Windows Malware Detection
Adrian Shuai Li, Elisa Bertino
Machine learning (ML)-based malware detectors degrade over time as concept drift introduces new and evolving families unseen during training. Retraining is limited by the cost and…
LLMalMorph: On The Feasibility of Generating Variant Malware using Large-Language-Models
Md Ajwad Akil, Adrian Shuai Li, Imtiaz Karim +4
Large Language Models (LLMs) have transformed software development and automated code generation. Motivated by these advancements, this paper explores the feasibility of LLMs in mo…
SoK: Leveraging Transformers for Malware Analysis
Pradip Kunwar, Kshitiz Aryal, Maanak Gupta +2
The introduction of transformers has been an important breakthrough for AI research and application as transformers are the foundation behind Generative AI. A promising application…