🌊Wake Framework as engine for AI

The Wake Framework serves as the foundation for Wakehacker's AI capabilities, providing comprehensive smart contract analysis through multiple approaches:

Core Capabilities

• Testing Framework: Python-based testing environment with fast execution (Testing Framework Overview)

• Static Analysis: Vulnerability detection with focus on minimizing false positives (Built-in Detectors)

• Fuzzing Framework: Advanced testing methodology including Manually Guided Fuzzing (Fuzzing Documentation, Manually Guided Fuzzing)

Internal Representation (IR)

Wake's IR model provides deep understanding of smart contracts (Working with IR):

• Complete control flow graph (CFG) analysis

• Data dependency graph (DDG) tracking

• Cross-contract relationships

• Storage layout verification (Storage Layout Printer)

• Function call hierarchies (Control Flow Graph)

Precision-First Approach

Wake prioritizes precision over recall in its analysis:

• Condition-based detection rather than heuristics

• Minimizes false positives through precise pattern matching

• Provides detailed context for each detection

• Enables efficient verification of findings

Measured Performance

Recent experiments comparing Wake's write-after-write detector with Slither (a widely-used heuristic-based analyzer) demonstrate its precision across two controlled studies:

Study 1: Controlled Test Suite Analysis of 34 purpose-built smart contracts containing both simple and edge cases:

Metric	Wake	Slither
Precision	100%	86.36%
Recall	76.19%	33.33%
F1-Score	86.59%	50%

Study 2: Production Contracts Analysis of 50 randomly selected smart contracts from a dataset of 9,388 production contracts:

Metric	Wake	Slither
Precision	100%	86.36%
Recall	94.74%	50%
F1-Score	97.29%	63.33%

These results demonstrate Wake's detection capabilities through its condition-based approach rather than heuristic methods, particularly in minimizing false positives while maintaining high recall rates. While these measurements focus on a single detector type, they illustrate the framework's potential when precise condition-based analysis is applied rather than heuristic approaches like those used in Slither.

These findings were presented at the 2024 TUM Blockchain Conference "Ethereum Vulnerability Detectors".

AI Integration

Wake's architecture enables AI enhancements through:

• Structured IR data perfect for LLM consumption

• Context-aware output slicing

• Relationship mapping for complex analysis

By providing exact information in the right context, Wake enables AI models to:

1. Understand complex contract relationships

2. Provide extended vulnerability descriptions

3. Explain potential impact and remediation

4. Verify mathematical calculations

5. Reduce false positive detections