Large Language Model-Assisted Smart Contract Vulnerability Detection: Breaking the Capabilities Boundaries of Static Analysis Tools

This article introduces an innovative security research project that uses large language models (LLMs) to detect semantic and logical layer vulnerabilities in Ethereum smart contracts that traditional static analysis tools cannot reach. By combining static preprocessing with LLM semantic reasoning, the project breaks through the capability boundaries of existing tools and provides a new direction for smart contract security audits.

Traditional static analysis tools (e.g., Slither, Mythril) rely on code structure pattern matching and can only detect Track A structurally detectable vulnerabilities (reentrancy attacks, external call DOS, etc.), but are helpless against Track B vulnerabilities that require deep semantic understanding (access control flaws, asset locking, logical errors). The core question of this project: Can multi-LLM collaboration detect such logical vulnerabilities? The research team gave an affirmative answer through analysis of 27 real contracts and 6 types of vulnerabilities.

The project adopts a phased architecture: ##1. Contract Parsing and Behavior Extraction Extract functions via regular expressions and bracket matching, build a lightweight intermediate representation, focusing on operation sequences, CEI order, permission checks, and external call signals. ##2. Candidate Function Filtering Filter functions that need LLM analysis based on keywords and heuristic rules to improve efficiency. ##3. Two-Stage LLM Reasoning

• Scenario Matching: Determine whether the function conforms to the vulnerability pattern
• Attribute Verification: Verify whether the risk actually exists Dual verification reduces false positive rates.

##4. Result Classification and Reporting Generate structured reports after priority sorting.

Detection Capability Comparison

Detection System	Track A Detected Count	Track B Detected Count
GPT-4 Pipeline	23/26	12/13
Slither	7/16*	0/13
Mythril	11/26	0/13
*Slither failed to parse 10 contracts due to import errors
Key Finding: Traditional tools detected zero Track B vulnerabilities, while the LLM pipeline successfully identified 12/13.

Model Comparison

GPT-4 (21/23) > Claude (20/23) > Gemini (19/23); GPT-4 excels in complex semantic reasoning.

Track A (Structurally Detectable)

• Reentrancy Attack: External call precedes state update
• External Call DOS: Critical path contains blocking external calls
• Delegate Call Abuse: Unprotected delegate call pointing to a controllable address

Track B (Semantic-Dependent)

• Subtle Access Control Flaws: Initialization function front-running, two-step ownership transfer errors
• Asset Locking Conditions: Conditions where user funds are permanently inaccessible
• Logical Verification Errors: Business logic flaws such as phase skipping, repeated initialization, etc.

Current Limitations:

• Computational Cost: Two LLM calls per function, high cost for large contracts
• Latency: Not suitable for real-time scanning
• Contract Complexity: Context window limits analysis of highly complex contracts
• Multi-Language Support: Only for Solidity Future Directions: Optimize candidate filtering, establish vulnerability pattern caching, code splitting and summarization techniques, expand multi-language support.

Practical Insights:

1. Pure static analysis tools have clear capability boundaries and need to be combined with semantic understanding methods
2. Human-machine collaboration model becomes mainstream: LLMs identify potential semantic vulnerabilities, and human experts verify and fix them
3. Open-source implementation provides a research foundation for the community and promotes industry technological progress This project proves the potential of LLMs in the field of smart contract security and helps build a more robust protection system.