02 / Applied security ML
Smart Contract Vulnerability Detection
A transformer-based static analysis system designed to classify vulnerability patterns directly from Solidity source code.
The challenge
Problem & approach
Manual smart contract audits are expensive and difficult to scale. This pipeline learns contextual code representations to flag likely vulnerabilities earlier in development, prioritizing contracts and code regions for expert review rather than replacing security auditors.
MODEL EVALUATION
Training performance
The displayed accuracy is the supplied training result; production assessment would additionally report held-out precision, recall, F1, and class-level error analysis.
System architecture
Normalize
Clean Solidity source while retaining security-relevant syntax and structural cues.
Tokenize
Encode contracts with CodeBERT’s code-aware tokenizer and bounded sequences.
Fine-tune
Train a classification head in PyTorch over contextual transformer embeddings.
Evaluate
Track accuracy and class-level behavior to surface likely vulnerability families.
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