CodeBERT Base Fine-tuned for Insecure Code Detection
| Property | Value |
|---|---|
| Paper | CodeBERT Paper |
| Dataset | CodeXGLUE Defect Detection |
| Accuracy | 65.30% |
| Training Data | 21,854 examples |
What is codebert-base-finetuned-detect-insecure-code?
This model is a specialized version of CodeBERT fine-tuned specifically for detecting insecure code patterns in software. It performs binary classification to identify potentially dangerous code that could lead to vulnerabilities such as resource leaks, use-after-free issues, and DoS attacks.
Implementation Details
Built on the CodeBERT architecture, this model leverages a Transformer-based neural network and has been trained using a hybrid objective function that incorporates replaced token detection. The model processes source code input and classifies it as either secure (0) or insecure (1).
- Trained on 21,854 code examples
- Validated on 2,732 examples
- Tested on 2,732 examples
- Achieves state-of-the-art accuracy of 65.30%
Core Capabilities
- Binary classification of code security
- Detection of resource leaks
- Identification of use-after-free vulnerabilities
- Recognition of potential DoS attack vectors
- Processing of both natural language and programming language inputs
Frequently Asked Questions
Q: What makes this model unique?
This model outperforms previous approaches including BiLSTM (59.37%), TextCNN (60.69%), and base CodeBERT (62.08%) with its 65.30% accuracy on insecure code detection tasks.
Q: What are the recommended use cases?
The model is ideal for automated code review processes, security auditing of codebases, and as part of a larger security testing pipeline. It's particularly useful for identifying potential security vulnerabilities during the development process.
