SemAder: Evading LLM-Based Binary Code Analysis via Structure-Semantics Joint Induction

SemAder is a technique that can fool LLM-based binary code analysis tools (AI systems trained to understand compiled machine code) by manipulating both the code's structure and its underlying meaning. The research, published in ACM Transactions on Privacy and Security, demonstrates that attackers can evade detection by simultaneously changing how the code is organized and what it actually does, making it harder for AI-powered security analysis to identify malicious behavior.