arXiv AI June 3, 2026 By Peter Williams, Adam Sobey, Erisa Karafili

High-Precision APT Malware Attribution with Out-of-Scope Resilience

arXiv:2606. 03523v1 Announce Type: cross Abstract: Early attribution of Advanced Persistent Threat (APT) activity can help defenders prioritise investigation, select countermeasures, and reduce the impact of an intrusion.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 13

Regression-aware Continual Learning for Android Malware Detection

arXiv:2507. 18313v2 Announce Type: replace Abstract: Malware evolves rapidly, forcing machine learning-based detectors to be continuously updated.

By Daniele Ghiani, Daniele Angioni, Giorgio Piras, Angelo Sotgiu, Luca Minnei, Srishti Gupta, Maura Pintor, Fabio Roli, Battista Biggio

arXiv Machine Learning

Jul 21

When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift

arXiv:2602. 14161v2 Announce Type: replace Abstract: Detecting prompt injection, jailbreak attacks, and harmful requests is critical for deploying LLM-based agents safely, yet current evaluation practices in this literature overestimate generalization.

By Max Fomin

llms agents benchmarks safety

arXiv AI

Jun 30

A Multi-task Mixture of Experts Framework for Malware Classification, Packing Detection, and Family Attribution

arXiv:2606. 30572v1 Announce Type: cross Abstract: Malware classification remains a challenging problem due to its inherent heterogeneity, the presence of packed binaries, and the diverse distribution of malware families.

By Jithin S., Roshin Sleeba C., Anvin Mariya P. B., Asmitha K. A., Vinod P., Serena Nicolazzo, Antonino Nocera

safety

arXiv AI

Jul 21

Is "Knowing It's Malicious Enough?" Evaluating LLMs for Fine-Grained Malware Behavior Auditing

arXiv:2509. 14335v2 Announce Type: replace-cross Abstract: Automated malware classifiers achieve strong detection performance, but auditing requires more than flagging a sample: analysts must explain malicious behaviors and justify them with code evidence.

By Xinran Zheng, Xingzhi Qian, Yiling He, Shuo Yang, Lorenzo Cavallaro

llms agents benchmarks

arXiv Machine Learning

Jun 17

Multi-Source Cybersecurity Logs: An ATT&CK-Labeled Dataset and SLM Evaluation

arXiv:2606. 18190v1 Announce Type: cross Abstract: Multi-stage cyberattacks span system, network, and browser logs.

By Abir Ashab Niloy, Ahmed Ryan, Imamul Hossain Rafi, Md Erfan, Md Rayhanur Rahman

llms fine-tuning safety

arXiv AI

5d ago

Concept Drift Detection and Adaptive Retraining of Malware Classification Models

arXiv:2608. 13465v1 Announce Type: cross Abstract: Concept drift refers to changes over time in the statistical properties of data, as compared to the data that was used to train a learning model.

By Christofer Washington Berruz Chungata, Martin Jurecek, Katerina Potika, William B. Andreopoulos, Mark Stamp

Related stories

Regression-aware Continual Learning for Android Malware Detection

When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift

A Multi-task Mixture of Experts Framework for Malware Classification, Packing Detection, and Family Attribution

Is "Knowing It's Malicious Enough?" Evaluating LLMs for Fine-Grained Malware Behavior Auditing

Multi-Source Cybersecurity Logs: An ATT&CK-Labeled Dataset and SLM Evaluation

Concept Drift Detection and Adaptive Retraining of Malware Classification Models