This repository maps reverse engineering techniques across domains, from software and electronics to aircraft and nuclear weapons. Every entry is compressed field knowledge from real-world engineering practice, packed into a problem-solution pair where the problems of tearing down undocumented systems meet the investigation techniques actually used to solve them. The repository is intentionally built as a mapping rather than a tutorial, so it can serve as a solid starting point whenever you face a black-box system.
| Category | Context |
|---|---|
| Aerospace | Aircraft, rockets, missiles, and out-of-production flight hardware |
| Biology | Gene regulatory networks, molecular pathways, and systems biology inference |
| Chemical | Product formulations, deformulation, and competitor composition analysis |
| Civil | Structures, load paths, and documentation of existing built assets |
| Design | Historical collections, craft processes, and re-modeling of existing designs |
| Electronics | Circuit boards, integrated circuits, smart cards, and embedded hardware |
| Forensics | Failure investigation, incident reconstruction, and evidence-driven root causes |
| Legal | Patent law overlap, clean room design, and lawful examination boundaries |
| Mechanical | Physical parts, machines, and scan-to-CAD geometric reconstruction |
| Military | Captured enemy technology, intelligence-driven duplication, and countermeasures |
| Nuclear | Fissile materials, detonation attribution, and device design inference |
| Security | Vulnerability discovery, product protection analysis, and access restriction testing |
| Software | Binaries, source code, protocols, cryptography, and malicious code analysis |
Pick the category document closest to the task at hand and attach it to an AI agent as context. Let the agent walk through the problem-solution table and apply the relevant techniques, so the approach follows real professional practice instead of guesswork. The entries also work as a checklist for manual investigation, for example when deciding which measurement tool, analysis method, or teardown order fits best.
All documents in this repository were written with the help of an LLM. The writing also doubles as an experiment to measure how well an AI model understands reverse engineering techniques at a specific level, and as material for analyzing the impact and limitations of applying those techniques in practice.
Warning
Reverse engineering can be dangerous and legally risky. Some techniques in this repository involve high-energy equipment, chemicals, radioactive materials, or legally protected systems. Use them only on your own property or with proper authorization, understand local regulations before experimenting, and always follow appropriate safety procedures. This repository is educational, and no responsibility is taken for misuse.
This repository is licensed under MIT.