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

Transformer-based malware detection systems operating on graph modalities such as control flow graphs CFGs achieve strong performance by modeling structural relationships in program behavior. However, their robustness to adversarial evasion attacks remains underexplored. This paper examines the...

LLM-Driven Feature-Level Adversarial Attacks on Android Malware Detectors

The rapid growth in both the scale and complexity of Android malware has driven the widespread adoption of machine learning ML techniques for scalable and accurate malware detection. Despite their effectiveness, these models remain vulnerable to adversarial attacks that introduce carefully crafte...

Attack AI systems in Machine Learning Evasion Competition

Today, we are launching MLSEC.IO, an educational Machine Learning Security Evasion Competition MLSEC for the AI and security communities to exercise their muscle to attack critical AI systems in a realistic setting. Hosted and sponsored by Microsoft, alongside NVIDIA, CUJO AI, VM-Ray, and MRG...

How to confuse antimalware neural networks. Adversarial attacks and protection

Introduction Nowadays, cybersecurity companies implement a variety of methods to discover new, previously unknown malware files. Machine learning ML is a powerful and widely used approach for this task. At Kaspersky we have a number of complex ML models based on different file features, including...

Examining & Evaluating Security Before a “Pressure Event” is Critical…Especially on a Hot Summer Day

There are countless parallels between cyber and physical security. I often use physical security to explain cyber to the uninitiated. The thick walls, soundproofed vents, locks and codes and even the key on the door to Robert Hanssen’s SCIF are mirrored by the malware detectors, firewalls next-ge...