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On Wed, 30 Oct, 12:08 AM UTC
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Security Debt Looms -- GitHub Copilot Autofix Steps In
According to IDC, 69% of developers cite frequent security-related context-switching as a hindrance, leading to security oversights, alongside impacting productivity. To solve this, GitHub Copilot today announced a new update to Copilot Autofix. Just as GitHub Copilot helps developers code more quickly, Copilot Autofix accelerates the pace of remediation so security teams make real progress with the backlog of existing vulnerabilities, commonly known as security debt. This new feature supports integration with various third-party tools and security campaigns, enabling security teams and developers to address vulnerabilities at scale using their preferred tools. This includes ESLint, JFrog SAST, and Black Duck's PolarisTM platform powered by Coverity®, so developers can streamline security workflows with their code scanning tooling of choice. This new feature is available today in public preview. For instance, the integration between JFrog and GitHub offers developers a seamless DevSecOps experience by bringing together JFrog's Advanced Security SAST and Runtime Security with GitHub's Copilot Autofix, enhancing automated vulnerability remediation and real-time runtime monitoring in GitHub workflows. As noted at GitHub Universe, this integration eliminates context-switching by allowing developers to "write, debug, and secure their code simultaneously," addressing industry pain points of productivity and security oversight. Since its introduction in public beta in March 2024, developers have used Copilot Autofix in their pull requests to help them quickly fix vulnerabilities in new code before they get merged into production where they can impact customers. Copilot Autofix in action: Behind the scenes, Copilot Autofix utilises the CodeQL engine, GPT-4o, and a combination of heuristics and GitHub Copilot APIs to generate code suggestions. Copilot Autofix builds an LLM prompt based on sources including CodeQL analysis and short snippets of code around the flow path. A recent GitHub study found that 97% of developers use AI coding tools, yet using AI to assess AI remains questionable. While GitHub Copilot Autofix employs automated testing, red team scrutiny, and filtering to mitigate risks, experts underscore limitations in self-verifying AI systems, suggesting that relying on another AI model for review may be fraught with redundancy and cost challenges. "It's hard to use AI to trust AI for the same reason people often miss their own mistakes," said David Timothy Strauss, CTO at Pantheon. Developers are now deploying software at an unprecedented pace, frequently rolling out new features. However, despite their commitment to secure coding, vulnerabilities still find their way into production, remaining a major cause of breaches. This challenge is intensified by the complexity of security requirements, which many developers struggle to grasp and apply effectively. As a result, achieving robust security remains difficult, leading to more vulnerabilities being released into the open. GitHub claimed that code scanning tools identify vulnerabilities but don't solve the core issue: fixing them requires specialised security knowledge and time -- both of which are scarce. The challenge isn't finding vulnerabilities, but resolving them. That is where Copilot Autofix comes into play. Team GitHub previously claimed that during the public beta, developers were able to fix code vulnerabilities over three times faster compared to manual efforts, demonstrating how AI agents can significantly streamline and accelerate secure software development.
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Copilot Autofix Gets New Superpowers with Third-Party Tools Integration!
Marking its 10th anniversary, GitHub Universe brings together several AI updates to GitHub Copilot. One of the features added is -- Security campaigns and third-party tool integration with Copilot Autofix. Just as GitHub Copilot helps developers code more quickly, Copilot Autofix accelerates the pace of remediation so security teams make real progress with the backlog of existing vulnerabilities, commonly known as security debt. This new feature supports integration with various third-party tools and security campaigns, enabling security teams and developers to address vulnerabilities at scale using their preferred tools. It fosters a collaborative environment where teams can seamlessly incorporate security measures into their existing workflows. By using familiar tools, this approach not only improves productivity but also helps maintain a consistent security posture across all projects, making it easier to manage as they arise. Since its introduction in public beta in March 2024, developers have used Copilot Autofix in their pull requests to help them quickly fix vulnerabilities in new code before they get merged to production where they can impact customers. Copilot Autofix was also planned to be available for all open-source projects. As the feature uses the CodeQL engine, Copilot APIs, and GPT-4o, it could be a highly valuable asset for various tech enterprises. Just as Copilot helps developers code more quickly, Copilot Autofix accelerates the pace of remediation so security teams make real progress with the backlog of existing vulnerabilities, commonly known as security debt. Vulnerabilities can linger indefinitely, becoming harder and costlier to fix over time. Copilot Autofix streamlines this process, helping developers quickly and confidently resolve issues in unfamiliar or outdated code. Behind the scenes, Copilot Autofix utilises the CodeQL engine, GPT-4o, and a combination of heuristics and GitHub Copilot APIs to generate code suggestions. Copilot Autofix builds an LLM prompt based on sources including CodeQL analysis and short snippets of code around the flow path. Developers are now deploying software at an unprecedented pace, frequently rolling out new features. However, despite their commitment to secure coding, vulnerabilities still find their way into production, remaining a major cause of breaches. This challenge is intensified by the complexity of security requirements, which many developers struggle to grasp and apply effectively. As a result, achieving robust security remains difficult, leading to more vulnerabilities being released into the open. Code scanning tools identify vulnerabilities but don't solve the core issue: fixing them requires specialised security knowledge and time -- both of which are scarce. The challenge isn't finding vulnerabilities, but resolving them. However, during the public beta, developers were able to fix code vulnerabilities over three times faster compared to manual efforts, demonstrating how AI agents can significantly streamline and accelerate secure software development. As developers remain responsible for software security, we believe that with Copilot Autofix at your side, every developer benefits from security expertise whenever they need it and security becomes simply synonymous with software development.
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GitHub introduces new features for Copilot Autofix, integrating third-party tools to address security vulnerabilities more efficiently. This update aims to reduce security debt and streamline the development process.
GitHub has announced a significant update to its Copilot Autofix feature, introducing integration with third-party tools to address the growing concern of security debt in software development. This new capability, revealed at GitHub Universe, aims to streamline the process of identifying and fixing vulnerabilities in code 1.
According to IDC, 69% of developers cite frequent security-related context-switching as a major hindrance to productivity and a contributor to security oversights 1. Despite developers' commitment to secure coding practices, vulnerabilities continue to find their way into production environments, remaining a significant cause of breaches. The complexity of security requirements often overwhelms developers, making it difficult to achieve robust security 1.
Copilot Autofix, introduced in public beta in March 2024, has already demonstrated its effectiveness in helping developers fix vulnerabilities in new code before merging into production. The latest update expands its capabilities by integrating with various third-party tools and security campaigns 2.
Third-Party Tool Integration: Copilot Autofix now supports integration with tools such as ESLint, JFrog SAST, and Black Duck's Polaris™ platform powered by Coverity® 1.
Accelerated Remediation: The update aims to speed up the process of addressing existing vulnerabilities, helping security teams make significant progress in reducing their backlog 2.
AI-Powered Suggestions: Behind the scenes, Copilot Autofix utilizes the CodeQL engine, GPT-4o, and a combination of heuristics and GitHub Copilot APIs to generate code suggestions 1.
The integration between JFrog and GitHub offers a seamless DevSecOps experience by combining JFrog's Advanced Security SAST and Runtime Security with GitHub's Copilot Autofix. This collaboration enhances automated vulnerability remediation and real-time runtime monitoring in GitHub workflows 1.
During the public beta, developers using Copilot Autofix were able to fix code vulnerabilities over three times faster compared to manual efforts, demonstrating the potential of AI in streamlining secure software development 2.
While the benefits of Copilot Autofix are clear, some experts have raised concerns about using AI to assess AI-generated code. David Timothy Strauss, CTO at Pantheon, noted, "It's hard to use AI to trust AI for the same reason people often miss their own mistakes" 1. GitHub addresses these concerns through automated testing, red team scrutiny, and filtering to mitigate risks 1.
As Copilot Autofix becomes available for all open-source projects, it has the potential to become a valuable asset for various tech enterprises. By making security expertise more accessible to developers, GitHub aims to make security synonymous with software development 2.
Reference
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GitHub has launched a new AI-powered feature to automatically fix code vulnerabilities. This tool aims to enhance security and streamline the development process for programmers.
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