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How AI enhances static application security testing (SAST)

Here’s how SAST tools combine generative AI with code scanning to help you deliver features faster and keep vulnerabilities out of code.

How AI enhances static application security testing (SAST)

In a 2023 GitHub survey, developers reported that their top task, second only to writing code (32%), was finding and fixing security vulnerabilities (31%).

As their teams “shift left” and integrate security checks earlier into the software development lifecycle (SDLC), developers have become the first line of defense against vulnerabilities.

Unfortunately, we’ve found that “shifting left” has been more about shifting the burden of security practices to developers, rather than their benefits. But with AI, there’s promise: 45% of developers think teams will benefit from using AI to facilitate security reviews. And they’re not wrong.

We spoke with Tiferet Gazit, the AI lead for GitHub Advanced Security, and Keith Hoodlet, principal security specialist at GitHub, to discuss security pain points for developers, the value of using an AI-powered security tool, and how AI enhances static application security testing (SAST).

Why are developers frustrated with security?

Before sharing insights from Gazit and Hoodlet, let’s hear from developers directly.

In late 2019, Microsoft’s One Engineering System team sat down with a handful of developers to understand their frustrations with following security and compliance guidelines. Though that was a few years ago, their pain points still resonate today:

  • When conducting security reviews, some developers are forced to use tools that weren’t designed for them, which negatively impacts their ability to find and address security vulnerabilities.
  • Also, the priority for most developers is to write and review code. Yet, in the age of shifting left, they’re also expected to review, understand, and remediate vulnerabilities as part of their day-to-day responsibilities.

When developers execute a program, they have everything they need in a run-time environment. Completing a security review is less straightforward. Often, developers need to exit their IDEs to view vulnerability alerts, research vulnerability types online, and then revisit their IDEs to address the vulnerability. This is what we call context-switching, and it can increase cognitive load and decrease productivity.

In short, security isn’t an inherent part of the development process, and developers often feel less confident in how secure their code is.

Without intervention, these frustrations will only increase over time. 75% of enterprise software engineers are expected to use AI coding assistants by 2028, according to Gartner. That means as developers improve their productivity and write more code with AI tools like GitHub Copilot, there will be even more code to review.

Security experts are stretched thin, too

It’s typically reported that for every 100 developers, there’s one security expert who ends up being the last line of defense against vulnerabilities (and is responsible for setting and enforcing security policies), which is a significant undertaking. While the exact numbers might vary, the ISC2 (International Information System Security Certification Consortium) reported a demand for four million more security professionals in its 2023 workforce study.

While AI doesn’t replace security experts, it can help them augment their knowledge and capabilities, especially when their expertise is in high demand.

“AI can help with those code and security reviews to ensure that increased momentum doesn’t lead to increased vulnerabilities,” Gazit says.

How AI enhances SAST tools

SAST tools aren’t the only kind of security tool used by developers, but they’re one of the most popular. Let’s look at how AI can help SAST tools do their job more efficiently.

Increased vulnerability detection

In order for SAST tools to detect vulnerabilities in code, they need to be shown what to look for. So, security experts use a process called modeling to identify points where exploitable user-controlled data enters and flows throughout a codebase. But given how often those components change, modeling popular libraries and frameworks is hard work.

That’s where AI comes in.

Security teams are experimenting with AI to model an extensive range of open source frameworks and libraries, improving the teams’ understanding of what’s inside of each software component.

Watch how Nick Liffen, director of GitHub Advanced Security, and Niroshan Rajadurai, VP of GTM strategy for AI and DevSecOps, show how AI could model unknown packages.

Contextualized vulnerabilities directly in a workspace

Code scanning autofix is an example of an AI-powered security feature that combines a SAST tool—in this case, GitHub’s CodeQL—with the generative AI capabilities of GitHub Copilot.

With code scanning autofix, developers receive an AI-suggested code fix alongside an alert directly in a pull request. Then, they get a clear explanation of the vulnerability and the fix suggestion, specific to their particular use case. To view and apply autofix suggestions directly in the CLI, they can enable the GitHub CLI extension.

In its first iteration, code scanning autofix analyzes and suggests fixes in JavaScript, TypeScript, Python, Java, C#, and Go. It can generate a fix for more than 90% of vulnerability types—and over two-thirds of those fixes can be merged with little to no edits. More languages like C++ and Ruby will be supported in the future.

The payoff is that developers can remediate vulnerabilities faster and in their workflows, rather than catching those vulnerabilities later in production.

A fortified SDLC

Developers use SAST tools to protect their code throughout the SDLC.

Once developers enable a code scanning solution like CodeQL, the SAST tool will scan your source code, integrating security checks as part of their CI/CD workflow:

  • When you make changes to a codebase and create pull requests on GitHub, CodeQL will automatically conduct a full scan of your code as if the pull request was merged. It will then alert you if a vulnerability is found in the files changed in the pull request.

    That means developers have the ability to continuously monitor the security posture of their source code as modules come together—even before changes are merged to their main branch. As a result, developers can remediate vulnerabilities right away, in development, and before their code is sent to production.

  • Outside of commits and pull requests, you can also set CodeQL to run at specified times in your GitHub Actions workflow. So, if you want CodeQL to regularly scan your code at specific time intervals, you can schedule that using a GitHub Actions workflow.

Are you already using code scanning autofix?

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See code scanning autofix in action

“Autofix makes CodeQL friendlier for developers by suggesting a fix and providing contextual explanations of the vulnerability and its remediation,” Gazit says. “This use of AI lowers the barrier of entry for developers who are tasked with fixing vulnerabilities.”

Let’s say a bad actor inserts a SQL injection into your application. The SQL injection enters your codebase through a user input field, and if the code comprising the injection exploits unintentional vulnerabilities, then the bad actor gets unauthorized access to sensitive data in your application.

SQL injections are a common type of vulnerability often found with a SAST tool.
Here’s a step-by-step look at how code scanning autofix, powered by GitHub Copilot, would detect a SQL injection and then surface it in an alert with an AI-suggested fix.

a flow chart against a dark background shows a SQL injection entering an application, the steps that GitHub's SAST tool CodeQL takes to trace the injection throughout a code base and generate an alert, and the steps that GitHub Copilot takes to augment that alert with an AI-generated fix and context.

Step 1: Hunt for vulnerabilities. Code scanning with CodeQL can be enabled for free on all public repositories and scheduled to run automatically. The scanning process has four main parts, all centered around your source code: tokenization, abstraction, semantic analysis, and taint analysis. Here’s a detailed breakdown of each of those steps.

In short, tokenizing your source code standardizes it, and that allows CodeQL to analyze it later. Abstracting your source code transforms your lines of code into a hierarchical structure that shows the relationship between those lines of code. Semantic analysis uses that abstraction to understand the meaning of your source code.

Finally, taint analysis looks at the way your source code handles user input data. It identifies data sources (where input data enters the source code), flow steps (where data is passed through the code), sanitizers (functions that make input data safe), and sinks (functions that if called with unsanitized data could cause harm). Advanced SAST tools like CodeQL can evaluate how well input data is sanitized or validated, and decide from there whether to raise the path as a potential vulnerability.

Step 2: Construct a prompt to generate a fix. For all languages supported by CodeQL, developers will see a SQL injection alert surfaced in a pull request in their repository, along with a natural language description of the vulnerability and contextual documentation. These alerts will also include a suggested fix that developers can accept, edit, or dismiss.

Here’s what’s included in the prompt, that’s sent to GitHub Copilot, to generate the enhanced alert:

  • The initial CodeQL alert and general information about the type of vulnerability detected. This will usually include an example of the vulnerability and how to fix it, extracted from the CodeQL query help.
  • Code snippets and line numbers, potentially from multiple source-code files, along the data flow identified during CodeQL’s taint analysis. These code snippets signal the places where edits are most likely needed in your source.

To guide the format of GitHub Copilot’s response, our machine learning engineers:

  • Constrain GitHub Copilot’s underlying model to only edit the code included in the prompt.
  • Ask the model to generate outputs in Markdown, including a detailed natural language explanation of the vulnerability and the suggested fix.
  • Ask for “before” and “after” code blocks, demonstrating the snippets that require changes (including some surrounding context lines) and the edits to be made.
  • Instruct the model to list any external dependencies used in the fix, such as data sanitization libraries.

Step 3: Check for undesirable code. Code snippets that match or nearly match runs of about 150 characters of public code on GitHub are then filtered from AI-generated coding suggestions. Vulnerable code, and off-topic, harmful, or offensive content are also filtered out.

You can explore the GitHub Copilot Trust Center to learn more about GitHub Copilot’s filters and responsible data handling.

Step 4: Apply finishing touches. Before developers see GitHub Copilot’s suggested fix, a fix generator processes and refines the LLM output to detect and correct any small errors.

The fix generator does this by:

  • Conducting a fuzzy search to ensure the “after” code blocks and line numbers, which contain the AI-generated suggested code fixes, match the “before” code blocks and line numbers. A fuzzy search looks for exact and similar matches between the code blocks, so the fix generator can catch and correct small errors, like those related to indentation, semicolon, or code comment differences between the two code blocks.
  • Using a parser to check for syntax errors.
  • Conducting semantic checks to evaluate the logic of the AI-suggested code fix. Name-resolution and type checks, for example, help ensure that the suggested code matches and maintains the intention and functionality of the original code.
  • Verifying any dependencies suggested by GitHub Copilot. This means locating the relevant configuration file containing information about the project’s dependencies to see if the needed dependency already exists in the project. If not, the fix generator verifies that the suggested dependencies exist in the ecosystem’s package registry, and checks for known vulnerable or malicious packages. It then adds new and needed dependencies to the configuration file as part of the fix suggestion.

Step 5: Explain the vulnerability and suggested fix. The final step is to surface the CodeQL alert to developers in a pull request. With code scanning autofix, the original CodeQL alert is enhanced with an AI-suggested fix, a natural language explanation of the vulnerability and suggested fix, and a diff patch. Developers can accept the suggested edit as is, refine the suggested edit, or dismiss it.

a  flow chart against a dark background details steps that show how a prompt to GitHub Copilot ultimately results in a security alert enhanced with an AI-suggested fix and additional context.

How developers, the SDLC, and organizations benefit from AI-powered SAST tools

With AI, security checks have the ability to smoothly integrate into a developer’s workflow, making security a feature of the SDLC rather than an afterthought dealt with in production. When developers can help secure code more easily in the development phase, the SDLC as a whole is hardened. And when the SDLC is better protected, organizations can focus more on innovation.

“When you treat security as a feature of the SDLC, your applications become more robust against increasingly complex attacks, which saves you time and money,” Hoodlet says. “You can direct those saved costs towards other improvements and experimentation with new features. The result? Organizations build a reputation for building secure products while freeing up resources for innovation.” Additionally, security teams are free to focus on the strategic initiatives that deserve their expertise.

Organizations that adopt AI-enhanced SAST tools can help developers to feel supported and productive in their security practices, so that developers can:

  • Help secure more code in development. Just look at the numbers. Code scanning autofix powered by GitHub Copilot can generate a fix for more than 90% of vulnerability types detected in your codebase, and more than two-thirds of its suggestions can be merged with little to no edits.
  • Become faster and better at remediating vulnerabilities. Through code scanning autofix, developers are given natural language explanations about an AI-generated code fix. They’re also given a description of the detected vulnerability that’s tailored to its detection in a specific codebase, rather than a general one. This specific context helps developers to better understand the nature of a detected vulnerability, why it exists in a codebase, and how to fix it.

  • Receive security guidance directly in their workspace. Developers receive all the benefits of an AI-enhanced SAST tool directly in a pull request. Unlike traditional security tools, this one is made for them.

Looking to secure your organization with the power of AI?

Learn more about SAST or get started today.

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