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20232026
most citedIntra-Section Code Cave Injection for Adversarial Evasion Attacks on Windows PE Malware File

4 citations · 6 across the 10 of their papers we have counts for

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12 papers · 1 filter

cs.CR2026

Malformer: A Multi-Modal Malware Detector Using Transformers

Samuel Howard, Kshitiz Aryal, Mahmoud Abdelsalam +3

Traditional malware detection systems that rely on a single representation of malware often fail to identify novel threats. These representations of malware binaries, also known as…

cs.CR2026

VEXAIoT: Autonomous IoT Vulnerability EXploitation using AI Agents

Katherine Swinea, Kshitiz Aryal, Lopamudra Praharaj +1

Internet of Things (IoT) systems are inherently vulnerable due to constrained hardware, outdated firmware, and insecure default configurations, creating a need for scalable and ada…

cs.CR2026

Explainability-Guided Adversarial Attacks on Transformer-Based Malware Detectors Using Control Flow Graphs

Andrew Wheeler, Kshitiz Aryal, Maanak Gupta

Transformer-based malware detection systems operating on graph modalities such as control flow graphs (CFGs) achieve strong performance by modeling structural relationships in prog…

cs.CR2026

A Survey of Agentic AI and Cybersecurity: Challenges, Opportunities and Use-case Prototypes

Sahaya Jestus Lazer, Kshitiz Aryal, Maanak Gupta +1

Agentic AI marks an important transition from single-step generative models to systems capable of reasoning, planning, acting, and adapting over long-lasting tasks. By integrating…

cs.CR2025

RAG-targeted Adversarial Attack on LLM-based Threat Detection and Mitigation Framework

Seif Ikbarieh, Kshitiz Aryal, Maanak Gupta

The rapid expansion of the Internet of Things (IoT) is reshaping communication and operational practices across industries, but it also broadens the attack surface and increases su…

cs.CR20252 cited

Safety and Security Analysis of Large Language Models: Benchmarking Risk Profile and Harm Potential

Charankumar Akiri, Harrison Simpson, Kshitiz Aryal +2

While the widespread deployment of Large Language Models (LLMs) holds great potential for society, their vulnerabilities to adversarial manipulation and exploitation can pose serio…