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

Computer Science > Cryptography and Security [Submitted on 29 May 2026] Investigating Detection and Obfuscation of Prompt Injection Attacks Against Software Reverse Engineering AI Agents Brian Crawford, Patrick McClure 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. Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE) Cite as: arXiv:2605.30677 [cs.CR]   (or arXiv:2605.30677v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2605.30677 Focus to learn more Submission history From: Patrick McClure [view email] [v1] Fri, 29 May 2026 00:13:35 UTC (870 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.AI cs.SE References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)