{"repo":"amruth-sn/kong","free":true,"listed":false,"github":"https://github.com/amruth-sn/kong","clone":"git clone https://github.com/amruth-sn/kong.git","description":"The world's first agentic reverse engineer.","language":"Python","stars":1083,"topics":["agentic-ai","ghidra","reverse-engineering"],"license":"Apache-2.0","category":"security-tools","readme_excerpt":"Kong: The Agentic Reverse Engineer LLM orchestration for reverse engineering binaries What is Kong? Most tasks follow a linear relationship: the more difficult a task, the longer it usually takes. Reverse engineering (and binary analysis) is a task in which the actual difficulty is somewhat trivial, but the time-to-execute can be on the order of hours (and days!), even for a binary with a couple hundred functions. Kong automates the mechanical layer, using an NSA-grade reverse engineering framework. Kong can take a fully obfuscated, stripped binary and run a full analysis pipeline: triaging functions, building call-graph context, recovering types and symbols through LLM-guided decompilation, and writing the results back into Ghidra's program database. The output is a binary where some FUN 00401a30 is now parse http header , with recovered structs, parameter names, and calling conventions. Why this exists Stripped binaries lose all the context that makes code readable: function names, type information, variable names, struct layouts. Recovering that context is the bulk of the work in most RE tasks, and it's largely pattern matching: recognizing standard library functions, inferring types from usage, propagating names through call graphs. LLMs are good at exactly this kind of pattern matching. But pointing an LLM at raw decompiler output and asking \"what does this do?\" gives you mediocre results. The model lacks calling context, cross-reference information, and the broader pict","default_branch":null,"files":null,"tree":[],"storefront":"/r/amruth-sn","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/amruth-sn/kong/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}