Breaking Windows Malware Detection: A Comprehensive Evaluation of Problem-Space Adversarial Robustness

Problem-space evasion attacks have exposed critical weaknesses in machine learning-based malware detectors; yet, their evaluation remains fragmented across models, datasets, and attack methodologies, often neglecting domain-specific requirements such as executability and functionality preservatio...

EXE-Bench: Ranking the Tradeoffs of AI-Based Windows Malware Detectors for Real-World Usability

Due to the lack of systematic evaluations, we are not yet able to determine which AI-based Windows malware detector to deploy in production, since existing evaluations i differ in terms of data used for both training and testing; ii do not consider temporal analysis to showcase whether models...

DroidBreaker: Practical and Functional Problem-Space Attacks on Machine-Learning Android Malware Detectors

Adversarial APKs are Android applications modified in the problem space to evade machine-learning malware detectors. In this work, we first show that, despite claims, existing problem-space attacks remain largely impractical. Most techniques leverage software transplantation to inject entire beni...

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...