Advancements in AI-Driven Reverse Engineering and Code Decompilation

Developments in large language models (LLMs) are increasingly impacting the field of reverse engineering, with users reporting successful applications in decompiling complex binaries and porting legacy software to modern programming languages.

Case Study: WebOS Media Stack Re-implementation

In a notable unverified report shared on Reddit, a developer utilized Claude to reverse-engineer the media stack of an LG webOS television. The project resulted in the creation of a native Rust-based Plex client for the 2019 hardware. According to the developer, the new implementation significantly improved performance metrics: the time required to load the profile screen was reduced from approximately 30 seconds to roughly 3 seconds, and the interface achieved a consistent 60 FPS. The custom client reportedly supports 4K resolution, Dolby Vision, and Dolby Atmos.

LLM Capabilities in Decompilation and Recompilation

Discussions within developer communities indicate that specific models are showing proficiency in “decomp/recomp” workflows—the process of converting machine code back into high-level source code.

Users have identified several models capable of these tasks, including Opus5.5, Sol6.1, Fable, and Astra. There is ongoing investigation into whether open-weight models, such as K3, GLM5.3, Qwen3.8-Max, and Mimo-2.6, can achieve similar results.

One user reported using GLM-5.3-flash to reverse-engineer a Win32 binary into a portable C++ codebase. The user noted that the model operated autonomously with tools such as Ghidra, radare2, and objdump, requiring no manual interaction with assembly code. However, the user cautioned that achieving “bug-for-bug” compatibility required significant human direction regarding testing and validation strategies, as the model occasionally attempted to “fix” bugs during the implementation process.

Technical Challenges and Methodologies

While LLMs demonstrate an ability to group components and suggest names for data structures, technical limitations remain. Experts in the field note that while models are effective at writing assertions and tests against synthetic Intermediate Representation (IR) lifted code, they often struggle with high-level code organization.

Current methodologies involve using an LLM to “hallucinate” plausible names for symbols and data structures, which are then refined by developers into modular, idiomatic code. The integration of LLMs with debuggers and emulators is also being explored to capture runtime behavior and internal states, allowing for cross-checking against the re-implemented code to ensure functional accuracy.