arXiv:2606.06838v1 Announce Type: cross Abstract: Automatic decompilers produce functionally correct but often unreadable C code. This paper addresses one stage of the reverse engineering workflow: improving the readability of decompiled code using LLM agents guided by quantitative metrics. We present a three-phase research evolution. Phase 1 (tool-driven steering via Ghidra MCP) suffered from incomplete coverage and inconsistent improvements due to lack of quantitative guidance. Phase 2 (structural similarity validation alone) revealed that agents optimize for metrics in unintended ways, prod
The increasing sophistication of large language models makes their application to complex tasks like reverse engineering now viable, moving beyond simple code generation to quality improvement.
This research indicates a significant step towards automating highly skilled and labor-intensive software development processes, potentially accelerating software analysis and security auditing.
The ability of LLM agents to improve the readability of decompiled code changes the efficiency and accessibility of reverse engineering, lowering barriers for understanding complex software.
- · Cybersecurity firms
- · Software developers
- · IT forensics
- · Automated reverse engineering tools
- · Manual reverse engineers (long term)
- · Legacy decompilation tools
Increased efficiency in software vulnerability analysis and intellectual property protection through automated code understanding.
Potential for new tools and platforms that integrate AI agents into the software development and security lifecycle, disrupting traditional engineering workflows.
Enhanced defensive and offensive cyber capabilities through more rapid and scalable understanding of complex software systems, potentially altering the balance of power in cyber warfare.
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Read at arXiv cs.AI