The RatHat Android trojan leverages AI to enable real-time device navigation and control, representing a shift in mobile malware sophistication. By integrating AI-driven automation, the malware can adapt its behaviour dynamically, making detection and remediation significantly harder for traditional security tools. This development signals a broader trend of threat actors embedding AI capabilities directly into offensive tooling.
Research highlighted by Bruce Schneier confirms that LLMs are highly effective at embedding hidden messages within seemingly normal text, a technique known as text-in-text steganography. This capability raises significant concerns for covert communications, data exfiltration, and the evasion of AI content moderation systems. Even small models with ~4 billion parameters demonstrate robust encoding and decoding of obfuscated language, lowering the barrier for adversarial misuse.
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