Prompt Injection Attacks on Reverse Engineering AI Agents
A new arXiv preprint investigates prompt injection attacks against agentic software reverse engineering systems. The research demonstrates defensive tactics for detecting prompt injection strings in decompiler output of adversarial binary programs. It also explores methods for obfuscating these attacks and subsequent defenses. The work advances understanding of risks and security for deploying such AI agents in production cyber workflows.
Key facts
- Agentic software reverse engineering systems are vulnerable to prompt injection attacks placed into source code of executable binary files.
- Research demonstrates defensive tactics for detecting prompt injection strings in decompiler output of adversarial example programs.
- Methods for obfuscating these attacks and subsequent defenses are explored.
- Research advances understanding of risk and security of agentic software analysis systems for deployment into production-level cyber workflows.
- Preprint is categorized under Computer Science > Cryptography and Security.
Entities
Institutions
- arXiv