Hybrid Decompilation Pipeline

• Hybrid decompilation pipeline is a method that merges static bytecode analysis with a fine-tuned LLM to translate EVM bytecode to Solidity.
• It reconstructs control flow, data dependencies, and semantic metadata via TAC recovery to enhance decompilation accuracy.
• Empirical evaluations show high semantic similarity and preservation of security-critical idioms compared to traditional decompilers.

A hybrid decompilation pipeline refers to a workflow that combines static program analysis with a domain-specialized LLM to transform low-level bytecode—specifically Ethereum Virtual Machine (EVM) bytecode in the context of smart contracts—into high-level, human-readable source code. This approach addresses the longstanding challenge of performing semantic analysis and security auditing when the vast majority of on-chain smart contracts (~99%) are closed-source, and existing decompilers fail to reconstruct intelligible or faithful source code from bytecode.

1. Motivation and Definition

A hybrid decompilation pipeline aims to bridge the gap between unstructured, stack-based bytecode and the original source code (Solidity) by orchestrating deterministic program analysis and neural code generation. The essential steps are:

1. Extraction of Low-Level Bytecode: Starting from the deployed contract’s EVM bytecode.
2. Static Analysis and Intermediate Representation (IR) Recovery: Employing static program analysis to identify functions, reconstruct control flow, and capture higher-level semantics in an IR.
3. LLM-Driven Source Code Synthesis: Using a fine-tuned LLM to generate high-level source code from the IR.
4. Validation and Post-processing: Ensuring the output is syntactically correct and semantically faithfulness is preserved.

This hybrid methodology uniquely recovers meaningful variable names, function signatures, and high-level control structures—which are often missing or obscured in previous decompilation attempts—while maintaining readably structured code suitable for security audits.

2. Static Program Analysis and TAC Recovery

The pipeline’s first phase involves rigorous static program analysis to convert stack-based EVM bytecode into a structured, compiler-like IR—three-address code (TAC):

• Function Boundary Recognition: Identifying function entry points and segmenting the contract bytecode into discrete routines.
• Control/Data-Flow Reconstruction: Constructing the control flow graph and data flow dependencies among variables and temporary storage locations.
• TAC Lifting: Translating EVM stack operations into TAC, where instructions each have the form:

$$\text{result} = \text{operand}_1 \;\text{op}\;\text{operand}_2$$

This step removes the stack manipulation complexity and makes explicit the computation graph necessary for high-level code reconstruction.

TAC encodes assignments, branching, and basic arithmetic and storage accesses in a way that closely resembles what a compiler frontend might produce. By abstracting the stack, it becomes possible to more accurately infer original variable groupings, control structures, and data types for subsequent code generation.

3. LLM-Based Code Generation and Fine-Tuning

The core innovation lies in the application of a domain-specialized LLM (Llama-3.2-3B) extensively fine-tuned on paired data: each example comprises the TAC of a smart contract function and its original Solidity implementation (if available). The training corpus consists of 238,446 such pairs drawn from verified contracts on the Ethereum mainnet.

• Input Formatting: TAC is serialized, preserving function structure and critical metadata (e.g., inferred types, storage layout, visibility).
• Instruction-to-Source Mapping: The LLM is trained to generate readable, idiomatic Solidity from non-trivial TAC, not only preserving logic but also recovering function and variable names, modifiers, and comment structure where possible.
• Adaptation via LoRA: Low-rank adaptation is used to adjust the pretrained LLM weights efficiently, focusing on the decompilation task’s peculiarities without overfitting or catastrophic forgetting.

The LLM thus learns to recognize bytecode patterns and their higher-level abstractions, enabling recovery of loops, conditional branches, event emission, and even complex constructs such as reentrancy guards or access-control logic.

4. Semantic Recovery and Quantitative Outcomes

The hybrid pipeline substantially advances semantic recovery compared to traditional rule-based or neural decompilers:

• Variable and Function Naming: The LLM, guided by TAC context, infers original or semantically meaningful names, enhancing code understandability.
• Control Flow Reconstruction: Beyond mere jump mappings, the pipeline produces structured if/else, for, and while blocks where feasible, suppressing the use of arbitrary goto or label-heavy forms.
• Function Signature Restoration: Signatures (including parameter types and visibility) are recovered, supporting static analysis and contract compatibility checks.

The evaluation demonstrates the following quantitative improvements:

• Semantic Similarity: 78.3% of functions show a code embedding similarity > 0.8 (cosine distance from the original source).
• Edit Distance: For 82.5% of outputs, the normalized Levenshtein edit distance to the original is below 0.4 (lower than previous decompilers).
• Preservation of Security-Critical Idioms: Solidity patterns such as require, assert, and sender/caller checks are accurately reconstructed in almost all test cases.

5. Comparative Perspective and Advantages

Compared to existing decompilers:

• Rule-Based Tools: Commonly fail to recover function boundaries, types, or present code in a structured, audit-ready format.
• End-to-End LLMs (without IR): Struggle with recovering real control flow and semantics from bytecode or disassembly alone, leading to illogical, hallucinatory outputs, especially for optimized or obfuscated contracts.

The hybrid pipeline is uniquely able to balance:

• Structural accuracy (via tacit program analysis), and
• Semantic expressiveness and readability (via neural code synthesis).

This duality translates in practice to source code better suited for vulnerability assessments, clone detection, and reverse engineering in security-critical workflows.

6. Deployment and Practical Applications

The pipeline is operationalized as a publicly accessible system (https://evmdecompiler.com) that enables anyone to input EVM bytecode and receive decompiled, readable Solidity code in return. The practical implications include:

• Security Auditing and Incident Response: Rapidly understanding closed-source smart contracts for bug or exploit investigation.
• Decompilation for Legacy and Obfuscated Contracts: Enabling porting, migration, or static analysis of legacy contracts.
• Foundation for Analytical Tooling: Providing high-quality source for further symbolic execution, property checking, or cross-contract analysis.

7. Limitations, Implications, and Future Directions

A remaining challenge is that certain highly optimized or highly obfuscated bytecode sequences may still resist faithful recovery, especially where type erasure or storage flattening occurs. The reliance on large, domain-specific training sets also raises questions about cross-domain and cross-architecture extensibility.

Future directions suggested by the results include:

• Incorporation of symbolic or partial dynamic analysis for further function boundary recovery and type inference.
• Scaling to Other Bytecode/VM Platforms: Adapting the static analysis + LLM pipeline to other smart contract ecosystems or binary domains (e.g., WebAssembly, eBPF).
• Enhancing Training Datasets: Mining more diverse, large-scale TAC-to-source pairs to improve model robustness for rare program idioms and security patterns.
• Hybrid Audit Workflows: Integrating the pipeline into vulnerability detection or automated property-based auditing platforms, potentially closing the automation loop from bytecode to security alert.

---

Stage	Method	Output/Improvement
Bytecode → TAC	Static analysis, control/data-flow	Structural clarity, compiler-like IR, recoverable logic
TAC → Solidity	Fine-tuned LLM (Llama-3.2-3B)	Human-readable, semantically faithful Solidity
Post-processing	Syntax and semantic validation	Syntactic correctness, improved auditability

---

The hybrid decompilation pipeline for EVM smart contracts, by fusing advanced static program analysis with domain-trained LLMs, establishes a new practical baseline for accurate, comprehensible, and semantically rich reverse engineering of closed-source and legacy smart contracts. Empirical results demonstrate superior semantic similarity and readability compared to prior decompilation methods, with immediate utility for blockchain security and analysis.