DroidBreaker: Practical and Functional Problem-Space Attacks on Machine-Learning Android Malware Detectors
arXiv:2606.26707v1 Announce Type: cross Abstract: 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 benign modules, introducing many side-effect features and often causing build-time failures. Fine-grained methods that inject only a narrow subset of components exhibit limited effectiveness, while those that also use obfuscation rely on brittle bytecode re
The paper highlights current limitations in adversarial attacks on AI malware detectors for Android, indicating a maturing arms race in cybersecurity and AI.
Sophisticated readers should care because effective adversarial attacks can compromise cybersecurity defences, leading to data breaches and system vulnerabilities, particularly relevant for mobile platforms.
Current machine-learning based Android malware detectors are shown to be more robust than previously claimed against 'practical' problem-space attacks, shifting the attack-defense balance, at least temporarily.
- · Cybersecurity industry (defenders)
- · Android users
- · ML model developers
- · Malware developers
- · Adversarial attack researchers
Increased confidence in current AI-driven Android malware detection systems.
Malware developers will focus on developing more advanced, stealthier problem-space attacks or shift to entirely new attack vectors.
Escalation of the AI cybersecurity arms race, driving further research into both AI defense and offense, potentially leading to more resilient yet complex security systems.
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Read at arXiv cs.LG