"GitHub Copilot Update Prevents AI Models from Unintentionally Exposing Secrets" - Credit: BleepingComputer

GitHub Copilot Update Prevents AI Models from Unintentionally Exposing Secrets

GitHub Copilot Update Stops AI Model From Revealing Secrets

GitHub has released an update to its Copilot tool that prevents artificial intelligence (AI) models from revealing sensitive information. The new feature, called Secret Scanning, is designed to help developers protect their code and data by scanning for secrets such as passwords, tokens, and keys.

The use of AI models in software development has become increasingly popular over the past few years. However, these models can be vulnerable to security threats if they contain confidential information such as credentials or API keys. To address this issue, GitHub created a tool called Copilot which scans source code repositories for potential security risks.

With the latest update to Copilot, GitHub is introducing a new feature called Secret Scanning which will scan for secrets within AI model files such as TensorFlow SavedModels (.pb), Keras HDF5 (.h5), and ONNX (.onnx). This will allow developers to detect any secrets that may have been inadvertently included in their model files before they are deployed into production environments.

In addition to detecting secrets within model files, Secret Scanning also provides users with detailed reports on where the secret was found and what type of file it was located in. This allows developers to quickly identify any potential issues so they can take corrective action before deploying their models into production environments. Furthermore, Secret Scanning also supports automated remediation actions so users can automatically fix any detected issues without having to manually edit their code or configuration files.

Secret Scanning is available now for all public repositories on GitHub at no additional cost beyond the existing pricing plans offered by GitHub Enterprise Cloud customers who already have access to Copilot’s featureset through an add-on subscription plan . Private repositories are not currently supported but support may be added in future updates depending on user feedback .

Overall , this new feature from GitHub helps ensure that sensitive information remains secure when using AI models during software development . By scanning for secrets within model files , developers can easily detect any potential vulnerabilities before deploying them into production environments . Additionally , automated remediation actions make it easier than ever before for users to quickly fix any detected issues without having manual intervention . With these tools at hand , organizations can rest assured knowing that their confidential data remains safe while leveraging the power of AI technology during software development projects .

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