AI-Powered Source Code Recovery from Binary Functions

Recovering source code from binaries has traditionally been a manual and tedious process, especially when dealing with stripped binaries (without debugging symbols) compiled with aggressive optimizations. However, advances in artificial intelligence and information retrieval techniques are transforming this field. A practical approach combines classic reverse engineering techniques with anchor-based search (strings, constants, external calls) and large language model (LLM) reasoning. This method does not aim to generate approximate pseudocode, but rather to identify the exact source function within a code database. The process begins by extracting anchors with tools like Ghidra, then performs an inverted index search to find candidate files, narrows down the list to likely function snippets, and finally reranks results using an LLM that analyzes disassembly, decompiled code, and source metadata. High-confidence matches can be used as new anchors in later passes, improving coverage. In an evaluation on an optimized, stripped tcpdump binary, 95.2% instruction coverage was achieved, while on noisy databases like GitHub average coverage was 35.5%, demonstrating that database quality is key. This type of solution has direct implications for businesses that need to maintain legacy systems, audit security, or migrate to the cloud. For example, an organization that has lost the original source code of a critical application can recover business logic, document vulnerabilities, and plan a modern migration. This is where services such as artificial intelligence and cybersecurity offered by Q2BSTUDIO become essential. The combination of AI-assisted reverse engineering with security practices allows not only understanding the code, but also strengthening the cybersecurity posture. Moreover, once the source code is recovered, it can be adapted to new cloud platforms like AWS or Azure, integrated with Business Intelligence (Power BI) systems, or even used to train AI agents that automate maintenance processes. Custom software development also benefits from this technology, as it allows reconstructing legacy logic for integration into new developments. In short, practical source code recovery from binaries with AI is not just a technical innovation, but a strategic enabler for companies seeking to digitize, secure, and modernize their software infrastructure. Q2BSTUDIO, with its expertise in software development, cloud, AI, and cybersecurity, is ready to accompany organizations in this process, offering solutions ranging from binary auditing to the implementation of intelligent agents that facilitate code governance.