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Challenges and Future Directions in Agentic Reverse Engineering Systems

arXiv Security Archived Apr 17, 2026 ✓ Full text saved

arXiv:2604.14317v1 Announce Type: new Abstract: Agentic systems built on large language models (LLMs) are increasingly being used for complex security tasks, including binary reverse engineering (RE). Despite recent growth in popularity and capability, these systems continue to face limitations in realistic settings. Cutting-edge systems still fail in complex RE scenarios that involve obfuscation, timing, and unique architecture. In this work, we examine how agentic systems perform reverse engin

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Computer Science > Cryptography and Security [Submitted on 15 Apr 2026] Challenges and Future Directions in Agentic Reverse Engineering Systems Salem Radey, Jack West, Kassem Fawaz Agentic systems built on large language models (LLMs) are increasingly being used for complex security tasks, including binary reverse engineering (RE). Despite recent growth in popularity and capability, these systems continue to face limitations in realistic settings. Cutting-edge systems still fail in complex RE scenarios that involve obfuscation, timing, and unique architecture. In this work, we examine how agentic systems perform reverse engineering tasks with static, dynamic, and hybrid agents. Through an analysis of existing agentic tool usage, we identify several limitations, including token constraints, struggles with obfuscation, and a lack of program guardrails. From these findings, we outline current challenges and position future directions for system designers to overcome from a security perspective. Comments: 7 pages, 1 figure, accepted at SAGAI 2026 Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI) MSC classes: 68T20, 68T01, 68M25 ACM classes: A.1 Cite as: arXiv:2604.14317 [cs.CR] (or arXiv:2604.14317v1 [cs.CR] for this version) https://doi.org/10.48550/arXiv.2604.14317 Focus to learn more Submission history From: Salem Radey [view email] [v1] Wed, 15 Apr 2026 18:20:07 UTC (101 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev | next > new | recent | 2026-04 Change to browse by: cs cs.AI 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?)

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