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

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 sof

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@misc{crawford2026investigating,
  title = {{Investigating Detection and Obfuscation of Prompt Injection Attacks Against Software Reverse Engineering AI Agents}},
  author = {Brian Crawford and Patrick McClure},
  year = {2026},
  month = may,
  eprint = {2605.30677},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2605.30677}
}