3 papers
cs.CR2026
Evasive Intelligence: Lessons from Malware Analysis for Evaluating AI Agents
Simone Aonzo, Merve Sahin, Aurélien Francillon +1
Artificial intelligence (AI) systems are increasingly adopted as tool-using agents that can plan, observe their environment, and take actions over extended time periods. This evolu…
cs.CR2025
Trust Under Siege: Label Spoofing Attacks against Machine Learning for Android Malware Detection
Tianwei Lan, Luca Demetrio, Farid Nait-Abdesselam +2
Machine Learning (ML) malware detectors rely heavily on crowd-sourced AntiVirus (AV) labels, with platforms like VirusTotal serving as trusted sources of malware annotations. But w…
cs.CR2024
How to Train your Antivirus: RL-based Hardening through the Problem-Space
Ilias Tsingenopoulos, Jacopo Cortellazzi, Branislav Bošanský +5
ML-based malware detection on dynamic analysis reports is vulnerable to both evasion and spurious correlations. In this work, we investigate a specific ML architecture employed in…