Beyond C/C++: Probabilistic and LLM Methods for Next-Generation Software Reverse Engineering

This proposal discusses the growing challenges in reverse engineering modern software binaries, particularly those compiled from newer system programming languages such as Rust, Go, and Mojo. Traditional reverse engineering techniques, developed with a focus on C and C++, fall short when applied to these newer languages due to their reliance on outdated heuristics and failure to fully utilize the rich semantic information embedded in binary programs. These challenges are exacerbated by the limitations of current data-driven methods, which are susceptible to generating inaccurate results, commonly referred to as hallucinations. To overcome these limitations, we propose a novel approach that integrates probabilistic binary analysis with fine-tuned large language models (LLMs). Our method systematically models the uncertainties inherent in reverse engineering, enabling more accurate reasoning about incomplete or ambiguous information. By incorporating LLMs, we extend the analysis beyond traditional heuristics, allowing for more creative and context-aware inferences, particularly for binaries from diverse programming languages. This hybrid approach not only enhances the robustness and accuracy of reverse engineering efforts but also offers a scalable solution adaptable to the rapidly evolving landscape of software development.