AI can help turn compiled binaries into more readable, sometimes recompilable code—but it cannot reliably restore the original source or prove that reconstructed code behaves the same. Recent papers show progress on specific benchmarks and repair workflows, not a general breakthrough across old software. The useful question is therefore not just whether AI can produce code, but how that code is checked.
What AI decompilation does—and does not—recover
Decompilation works backward from compiled machine code to a higher-level, source-like representation. A conventional workflow disassembles the binary into instructions, then uses static analysis to infer control flow, types, and pseudocode. The result can help a person understand a program, but it is not the original source: compilation may discard variable names, comments, and other source-level information, while optimization and ambiguity make exact reconstruction difficult. Conventional decompilers often prioritize readable pseudocode, which may not compile or run as-is.
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AI adds ways to generate or refine that representation. A model may translate binary code directly into source-like code, improve pseudocode produced by a conventional decompiler, or help repair output that does not compile. Each approach can make a binary easier to inspect, but its output remains an interpretation rather than a recovered original.
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Direct binary-to-code generation
A model trained for decompilation attempts to map binary code to a higher-level language. The goal is code a person can understand and, depending on the method and evaluation, potentially compile and execute. The model must infer information that compilation erased or obscured, so plausible-looking output is not proof that those inferences are correct.
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Refining a conventional decompiler’s output
Instead of starting from binary code alone, a refinement model takes pseudocode from a tool such as Ghidra and tries to make it clearer or more useful. This combines conventional analysis with language-model generation. The result may be more readable or easier to build, but edits can also change behavior.
Iterative compile-and-repair
Repair systems use feedback: attempt to compile or run the reconstructed code, give errors or other feedback to a model, and ask it to revise the output. Repeating the loop can fix build failures. A successful build, however, only establishes that the code meets the compiler’s requirements; it does not establish that the code matches the binary’s behavior.
What the published results show
The reported figures measure different tasks and benchmarks. They should not be read as a shared accuracy score or as a forecast for an arbitrary legacy application.
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| Work and year | Reported result | What it measures—and what it does not |
|---|---|---|
| LLM4Decompile, 2024 | The authors report over 100% higher re-executability than GPT-4o and Ghidra on HumanEval and ExeBench. | This is the authors’ comparison on those benchmarks and their re-executability measure, not a claim that the system is generally twice as accurate or can recover any old application. Their LLM4Decompile-Ref refinement approach also reports a 16.2% further improvement over LLM4Decompile-End, within the paper’s comparison. |
| DecLLM, 2025 | The authors describe an upper bound near 70%—70 of 100 originally non-recompilable outputs—as recompiling after iterative repair in their evaluation. | The evaluated repair loop used GPT-3.5 and GPT-4. Recompilation is a buildability result, not proof of semantic fidelity or a 70% recovery rate for old programs. |
| DecompileBench, 2025 | Hex-Rays had a reported average recompilation success of 58.3% in the paper’s evaluation of 12 decompilers. | This figure belongs to that benchmark and its evaluation. It is not directly comparable to LLM4Decompile’s results, which use different tasks and measures. |
| Liu, Raff, and Micinski preprint, 2026 | Among candidate outputs that passed every shipped test, the authors report 4.9% overall divergence on additional inputs in their input corpus, reaching 13% for one system. | In a separate evaluation, Ghidra’s build rate rose from 75% to 90% after the strongest refinement model, while matched behavioral rate fell from 74% to 62%. These are distinct measures in the preprint, not general rates for software decompilation. |
The 2026 work is a preprint, and its results apply to the systems and evaluations it describes. Its central caution is broadly relevant to interpreting decompilation results: passing a finite test set can leave behavioral differences undiscovered. The authors also report cases where vulnerability-related behavior was absent from reconstructed output.
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Which approach fits the job?
| Approach | Where it can help | Important limitation |
|---|---|---|
| Conventional decompiler, such as Ghidra or Hex-Rays | Structured pseudocode and established analysis workflows; DecompileBench’s authors favor established tools for reliability-critical debugging and performance analysis. | Readable output may still be difficult to compile or execute, and it is not the original source. |
| Direct AI decompiler | Generating source-like code from binary code, with benchmark results showing promise for specific evaluated tasks. | Results depend on the evaluated binaries, architectures, compiler settings, and metric; polished output can still be wrong. |
| AI refiner | Improving the readability or buildability of conventional decompiler output. | Refinement can alter behavior even when it improves build rates. |
| Iterative repair system | Using compiler or runtime feedback to address errors in an initial reconstruction. | Feedback checks only what it exercises. A program that compiles or passes a limited test suite may still diverge elsewhere. |
DecompileBench’s preference for conventional tools in reliability-critical analysis and AI approaches for faster comprehension is a task-specific conclusion from that evaluation, not a universal ranking. For quick orientation, AI-generated output can be a useful lead. For debugging, performance work, or security decisions, treat it as a hypothesis to verify against the binary and other evidence.
How to validate reconstructed code
Use the output to guide investigation, not as a substitute for the program you are investigating. A practical review should separate several questions that are easy to conflate:
- Readability: Can an analyst follow the control flow and understand the apparent purpose of the code? Clearer names or structure are useful, but may be inferred rather than recovered.
- Buildability: Does the output compile in the relevant environment? This checks syntax and build constraints, not behavioral equivalence.
- Execution: Does it run, and which tests or inputs have actually been tried? Record the test scope rather than treating a pass as universal.
- Behavioral agreement: Compare outputs, side effects, errors, and boundary cases against the original binary across inputs that are not limited to the supplied tests.
- Evidence scope: Check which processor architecture, binary type, compiler settings, and program corpus the method was evaluated on. Results from a benchmark do not automatically transfer to a different legacy program.
- Security implications: Independently inspect paths involving validation, permissions, parsing, and error handling. A reconstruction may omit behavior that matters to a vulnerability assessment.
For security triage, keep the original binary and conventional analysis in the loop. An AI reconstruction can suggest where to look, but absence of a suspicious operation in generated code does not establish its absence from the binary. Likewise, a plausible vulnerability fix or explanation should be checked against actual execution and the underlying instructions before it informs a decision.
Is AI decompilation legal?
There is no single yes-or-no answer for every program or jurisdiction. In the United States, 17 U.S.C. §1201(f) provides a narrow, conditional provision related to interoperability. It concerns a person who lawfully obtained the right to use a program and circumvents an access measure solely to identify and analyze elements needed for interoperability with an independently created program, when those elements are not readily available and the acts do not infringe. The statute also limits sharing certain information and tools under this provision. Its stated purpose includes “sole purpose of identifying and analyzing those elements of the program that are necessary to achieve interoperability.”
17 U.S.C. §1201 does not make all reverse engineering lawful, nor does it make AI decompilation categorically unlawful. The U.S. Copyright Office’s 2024 Section 1201 proceeding describes the broader restrictions on circumvention and the process for limited, temporary exemptions. Contracts, copyright, access controls, purpose, and other applicable law can matter; these sources do not resolve an individual case. Get jurisdiction-specific legal advice when the stakes require it.
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