Investigating Detection and Obfuscation of Prompt Injection Attacks Against Software Reverse Engineering AI Agents

2026-05-28

AI Summary

This study addresses the vulnerability of AI-powered software reverse engineering agents to prompt injection attacks by presenting the first systematic investigation of adversarial prompt injections embedded within executable binaries and their obfuscated variants. The work proposes an integrated defense framework that combines static analysis, specialized detection algorithms, and deobfuscation techniques to effectively identify diverse prompt injection attacks in decompiled output. Experimental results demonstrate that the proposed approach maintains high detection accuracy even against heavily obfuscated code, significantly enhancing the security and robustness of AI-driven reverse engineering systems in real-world operational environments.

Abstract

Agentic software reverse engineering systems are vulnerable to prompt injection attacks placed into the source code of executable binary files. This research demonstrates defensive tactics for detecting the presences of prompt injection strings in the decompiler output of adversarial example programs. Methods for obfuscating these attacks and subsequent methods for defending against these obfuscations are also explored. This research advances the understanding of risk and security of agentic software analysis systems necessary for their deployment into production-level cyber workflows.

Problem

• prompt injection
• software reverse engineering
• AI agents
• adversarial attacks
• security

Innovation

• prompt injection
• software reverse engineering
• AI agents
• attack obfuscation
• decompiler output