Reverse Engineer Anything (REA): From Fundamentals to Production Workflows

A hands-on, evidence-first guide to agent-assisted reverse engineering: REA setup, CLI/MCP workflows, static and dynamic analysis, native and Electron examples, Python/TypeScript test code, production runbooks, and careful interpretation of recent benchmarks.

A useful mental model has four layers.
-  turns a question into an investigation plan, chooses follow-up questions, interprets findings, and writes or tests a replacement implementation.
-  exposes repeatable commands and tool operations. MCP lets compatible agents call the analysis capabilities without inventing a new integration for each tool.
-  inspect the target. Depending on the task, this may include a native analysis engine, a static JavaScript artifact analyzer, a browser observation adapter, or a controlled process-capture workflow.
-  connect findings to an identified artifact, location, provider, observation or inference status, coverage, and limitations.

The important design choice is not simply “give an LLM more tools.” It is to make tool results traceable and reusable. A conclusion should be explainable in terms of the functions, strings, instructions, modules, captures, or other evidence that supports it. When a provider cannot establish a fact, the result should remain unknown or unavailable rather than being silently treated as false.

Failure modes and a practical decision rule

REA's output quality is bounded by the artifact, analysis provider, coverage, and investigation plan. Stripped symbols, obfuscation, indirect calls, dynamic loading, anti-analysis, unsupported architectures, missing runtime access, and incomplete captures can leave important questions unresolved.

Common failure patterns:

• Name anchoring: a function or string looks relevant but is not on the executed path. Follow references and validate with tests.
• Context flooding: the agent collects too much pseudocode and loses the control-flow thread. Summarize and fetch selectively.
• Version mismatch: evidence from an old binary is applied to a new build. Check artifact identity and hashes.
• Static/runtime contradiction: static code suggests one behavior but the observed app does another. Check feature flags, environment, network responses, build mismatch, and unobserved branches.
• False completeness: a partial source selection is treated as full application coverage. Read the coverage and limitations fields.
• Provider assumptions: a command is copied from an older README or a provider is missing. Use the installed version's help and diagnostics.
• Overconfident reconstruction: the new implementation passes the happy path but diverges on errors or state. Use a differential corpus and report the untested scope.

Use REA when you need structured assistance exploring an authorized target and can validate the result. Do not rely on it alone for high-stakes security conclusions, legal determinations, claims of complete source recovery, or proof that two programs are equivalent.

The operating principle is simple: the agent proposes, the evidence supports or rejects, and tests decide what has actually been validated.