Separating Secrets from Placeholders: A Hybrid CNN-CodeBERT Framework for Three-Class Credential Leakage Detection
Detect credential leakage in source code using a hybrid CNN-CodeBERT framework, reducing false positives by distinguishing between secrets, placeholders, and weak credentials
- Implement a hybrid CNN-CodeBERT framework to classify credentials into three categories: secrets, placeholders, and weak credentials
- Train the model using a dataset of labeled credentials to improve its accuracy
- Use the trained model to scan public source code repositories for potential credential leakage
- Configure the framework to integrate with existing CI/CD pipelines for automated detection
- Test the framework using a validation dataset to evaluate its performance and reduce false positives
Security engineers and developers can benefit from this framework to improve the security of their codebases and reduce the risk of credential leakage. This framework can be integrated into CI/CD pipelines to automate the detection process
💡 A three-class classification framework can effectively distinguish between secrets, placeholders, and weak credentials, reducing false positives and improving the security of codebases
🚨 Detect credential leakage in source code with a hybrid CNN-CodeBERT framework! 🚨
Key Takeaways
Detect credential leakage in source code using a hybrid CNN-CodeBERT framework, reducing false positives by distinguishing between secrets, placeholders, and weak credentials
Full Article
Abstract:
arXiv:2605.31520v1 Announce Type: cross Abstract: Credential leakage in public source code repositories poses a critical security threat, with over 23.8 million secrets exposed in 2024 alone. Existing detection tools suffer from high false-positive rates because rigid pattern matching and binary classification schemes fail to distinguish genuine credentials from placeholder or weak credentials. We propose a three-class classification framework that explicitly models placeholder or weak credentia
DeepCamp AI