LLM-Refined Decompile Tool

• LLM-refined decompile tool is a system that integrates large language models to iteratively refine traditional decompiler outputs, enhancing code correctness and semantic accuracy.
• It employs end-to-end translation, static and dynamic error feedback, and hybrid approaches to generate syntactically recompilable and semantically faithful source code.
• Applications span vulnerability analysis, software maintenance, and legacy code reconstruction, addressing limitations of conventional decompilers.

A LLM–Refined Decompile Tool is a system or methodology that integrates LLMs into the process of translating low-level program representations (such as assembly or intermediate code) back into readable, compilable, and semantically faithful high-level source code. These tools are often designed to not merely automate pattern extraction—like classical decompilers—but also to iteratively refine decompiler outputs, increasing code correctness, readability, and suitability for downstream analysis or deployment. The utilization of LLMs addresses long-standing challenges in reverse engineering, vulnerability analysis, and software maintenance, particularly where existing decompilers fall short in functional recovery and human-centric code representation.

1. Background and Motivation

Traditional decompilers operate by applying hand-crafted rules and patterns to recover control flow, variable assignments, and high-level abstractions from binaries or low-level code. While this approach has proven useful for reconstructing control-flow graphs and obtaining “pseudo-source” for reverse engineering, it is limited by brittleness to compiler optimizations, inability to generalize to new idioms, and high manual maintenance cost (Katz et al., 2019). Furthermore, existing decompiler outputs often lack reconcilability and semantic correctness—for example, outputs from leading tools such as IDA-Pro are not typically recompilable without extensive manual effort, as they suffer from type inference errors, missing symbols, and syntactic ambiguities (Wong et al., 2023).

LLM-refined decompile tools reframe decompilation through the lens of machine translation and generative modeling. With the ability to model broad programming concepts and encode both syntax and semantics, LLMs can systematically repair, post-process, or directly generate high-level code from low-level input, creating new opportunities and benchmarks in decompiler research (Tan et al., 8 Mar 2024, She et al., 17 Jun 2024).

2. System Architectures and Methodologies

Approaches to integrating LLMs into decompilation generally fall into three broad paradigms: end-to-end LLM decompilation, LLM-augmented refinement of existing decompiler outputs, and hybrid methods leveraging both static analysis and neural postprocessing.

End-to-End LLM Decompilation

Direct translation from binary or assembly code to source code is modeled as a sequence-to-sequence problem. The assembly (or IR) is tokenized, possibly canonicalized (e.g., numbers split into digits, post-order representations), and transformed into high-level source code through a neural decoder with attention mechanisms (Katz et al., 2019, Tan et al., 8 Mar 2024). Innovations in this space include:

• Preserving control flow via relabeling of jump targets and embedding CFG (Control Flow Graph) information in the prompt (Liu et al., 10 Mar 2025, Feng et al., 17 Feb 2025).
• Handling variable and literal recovery by linking data labels to values extracted from binary sections (“Function Call” strategy) (Feng et al., 17 Feb 2025).
• Extending the approach to multi-architecture or special-purpose domains, such as smart contracts (EVM to Solidity (David et al., 24 Jun 2025)) and WebAssembly (wat-to-C, with variable renaming and code slicing (She et al., 17 Jun 2024)).

LLM-Augmented Refinement

An alternative paradigm is to run a traditional decompiler (Ghidra, IDA-Pro, etc.) and then iteratively refine the output using LLMs. This yields two main phases (Wong et al., 2023, Tan et al., 8 Mar 2024):

• Static Augmenting: The decompiled (but non-compilable) code is fed to a compiler. Error messages generated by failed compilation attempts are provided as input to an LLM, which revises the code to address syntax and type errors, iteratively looping until a recompilable version is obtained.
• Dynamic Repairing: The resulting executable is instrumented (e.g., with AddressSanitizer) to detect runtime or memory errors, which again guide LLM-based corrections.

Hybrid and Context-Enhanced Approaches

Recent techniques augment the decompilation process by integrating semantic and structural context:

• Construction of Dependency Graphs and explicit prompt engineering: static analysis extracts control/data/type dependencies, which are encoded in chain-of-thought prompts to the LLM (Liao et al., 15 Jan 2025).
• Use of self-constructed context: decompiled output is recompiled, re-disassembled, and used as “in-context learning” to provide the LLM with ground truth pairings to resolve ambiguities (Feng et al., 25 Jun 2024).
• Fine-grained alignment, leveraging DWARF debug information to align source code blocks to assembly at the statement level during LLM fine-tuning, improving correspondence (Feng et al., 25 Jun 2024).
• Joint code and type definition prediction: simultaneously recovering both user-defined types and function implementations via extended sequence modeling (Dramko et al., 6 Feb 2025).

3. Performance Evaluation, Benchmarks, and Metrics

LLM-refined decompile tools are evaluated along multiple axes:

• Re-compilability: Whether the decompiled source passes the compiler, indicative of syntactic and structural correctness (Tan et al., 8 Mar 2024).
• Re-executability: Whether the recompiled binary, when executed, reproduces the original behavioral semantics (e.g., via test assertions). Strong results are observed in LLM4Decompile (e.g., >100% improvement in re-executability over Ghidra and GPT-4o on HumanEval/ExeBench) (Tan et al., 8 Mar 2024).
• Readability and Edit Similarity: Code proximity (token-level or AST-level) to the ground truth source, advised to be interpreted with caution due to data leakage concerns (Tan et al., 8 Mar 2024, She et al., 17 Jun 2024).
• Code Inflation and Structural Recovery: For example, WaDec reduces code bloat dramatically compared to baseline (3.34% vs. 116.94%) and yields improvements in AST edit distance and cyclomatic complexity (She et al., 17 Jun 2024).
• Human-Centric Assessment: Tools such as DecompileBench employ LLM “as judge” frameworks to rate outputs along control flow clarity, meaningful identifiers, and type-cast correctness, with scores aggregated across criteria (e.g., Elo ratings) (Gao et al., 16 May 2025).

Key large benchmarks include DecompileBench (23,400 real-world functions) for human-centric and runtime-aware assessment (Gao et al., 16 May 2025) and Decompile-Bench (2 million mapped binary-source function pairs) for function-level mapping and re-executability (Tan et al., 19 May 2025). Benchmarks typically incorporate multiple optimization levels and architectures to better simulate real-world complexity.

4. Key Innovations and Technical Advances

LLM-refined decompile tools introduce several technical innovations:

Component	Notable Advances	Source
Control flow preservation	Relabeling jump targets, CFG inclusion in prompts	(Liu et al., 10 Mar 2025, Feng et al., 17 Feb 2025)
Variable recovery	Function call to retrieve literals from binary	(Feng et al., 17 Feb 2025)
Type definition restoration	Joint code + type generation (Idioms, Realtype dataset)	(Dramko et al., 6 Feb 2025)
Self-improvement loop	Recompilation and dynamic context construction	(Feng et al., 25 Jun 2024)
Readability + accuracy RL	Reward-driven LLM fine-tuning (D-SCORE, D-LiFT framework)	(Zou et al., 11 Jun 2025)
Cross-domain extensibility	Smart contracts, WebAssembly, cross-lang decompilation	(Liao et al., 15 Jan 2025, David et al., 24 Jun 2025, She et al., 17 Jun 2024)

Innovations in prompt engineering (e.g., explicit JSON serialization of CFGs) allow even smaller LLMs to outperform much larger models on re-executability and semantic similarity, highlighting the importance of domain-focused context (Liu et al., 10 Mar 2025). Joint modeling approaches, as with Idioms, uniquely enable type-accurate decompilation in code with complex UDTs, addressing a historical gap (Dramko et al., 6 Feb 2025).

LLM-refinement also finds utility in domains with extremely limited source availability, such as smart contracts. Leveraging intermediate representations (e.g., three-address code for EVM bytecode) and LLM-guided recovery allows for functionally correct, readable Solidity code with high semantic similarity to the original, supporting vulnerability analysis and code auditing (David et al., 24 Jun 2025).

5. Challenges, Limitations, and Trade-offs

Despite rapid progress, several challenges persist:

• Functionality vs. Readability: LLM-refined decompilers often produce higher-quality, more readable output (surpassing commercial tools such as Hex-Rays on code understandability), but functionality correctness (as measured by re-executability and branch/side-effect coverage) currently lags by 52.2% on average (Gao et al., 16 May 2025). This trade-off underscores the need for further integration of traditional semantic reasoning.
• Hallucination and Error Propagation: LLMs may introduce subtle, semantically significant errors, especially in type inference, pointer arithmetic, or long-context prompts (Wong et al., 2023). Template-filling or reinforcement learning–guided reward systems are proposed to penalize inaccuracy in final outputs (Zou et al., 11 Jun 2025).
• Data and Benchmarking: Building high-quality, large-scale, and precisely mapped binary-source datasets is complex due to issues such as inlining, missing debug information, and code duplication (Tan et al., 19 May 2025). Source–trace algorithms and rigorous deduplication are critical for eliminating ambiguous examples.
• Obfuscation and Security: Techniques such as control-flow flattening, bogus code insertion, and junk bytes remain challenging for LLMs, though specialized methods (e.g., LLM-based validity classifiers) are beginning to make inroads into such obfuscated binaries (Rong et al., 12 Jul 2024).
• Resource Efficiency and Privacy: Model size, inference efficiency, and privacy concerns motivate the development of lightweight models (e.g., CIM-1.3B/6.7B outperforming much larger models when guided by structured context) (Liu et al., 10 Mar 2025).

6. Applications and Future Directions

LLM-refined decompile tools have established a foundation for several practical and research-oriented applications:

• Security: Automated vulnerability detection, incident forensics, and malware analysis are facilitated by enhanced decompilation that exposes subtle logic and state inconsistencies (Liao et al., 15 Jan 2025, David et al., 24 Jun 2025, Wang et al., 3 Sep 2025). The integration of execution traces and LLM-refined code in workflows such as TraceLLM achieves 85.19% precision in attacker/victim identification and 70.37% factual accuracy in incident reporting against ground-truth reports (Wang et al., 3 Sep 2025).
• Software Maintenance and Re-engineering: Improved code readability and semantic recovery directly support legacy code migration, binary patching, and IP recovery, particularly in situations where source code is unavailable.
• Cross-Domain and Educational Utility: As techniques are generalized (e.g., to WebAssembly, smart contract bytecode, multi-language support (She et al., 17 Jun 2024, Liao et al., 15 Jan 2025)), the LLM-refined workflow supports new domains for both automated program analysis and education.
• Hybrid and Interactive Systems: Hybrid systems combining IR lifting, static analysis, symbolic execution, and LLM postprocessing are likely to yield continued stepwise improvements, as are systems supporting interactive refinement through human–LLM collaboration for high-assurance tasks.
• Dataset Curation and Benchmarking: Automation frameworks such as CodableLLM streamline dataset generation for further training and evaluation, ensuring continued progress driven by real-world data (Manuel et al., 2 Jul 2025).

A plausible implication is that future decompilation pipelines will combine large-scale pre-trained models, explicit program structure extraction, RL-driven or iterative repair, and domain-specific evaluation, ultimately closing the functionality gap while further enhancing human-centric code reconstruction. The trend towards modular, extensible, and context-rich LLM-refined decompilation architectures is likely to accelerate as benchmarks, datasets, and model families diversify in support of increasingly complex real-world reverse engineering needs.