AI-Powered Fuzzing: The Future of Automated Application Security Testing

AI-Powered Fuzzing: The Future of Automated Application Security Testing

Increasingly complex code, evasive attackers, and other factors make application security essential during and after app development. Throughout the process, developers should be testing the application for bugs and security vulnerabilities to protect against downtime, whether that downtime is caused by unintentionally problematic inputs or deliberate attacks.

This is where application security testing tools come in. Throughout development and after release, it’s important to continually evaluate the application. Given the size and complexity of applications, however, this can be challenging. To address this problem, some organizations are turning to AI-powered fuzzing, a technique that helps find those bugs before a customer or attacker does.

Evolution of Fuzzing Techniques

One of the most effective ways to find vulnerabilities in an application is to model an attack. While theorizing about potential attacks to protect against them is important, a test that puts the app under stress can demonstrate better where there are flaws. Additionally, it’s better to find out about the flaw because you are attacking your own app than because someone else is.

Fuzzing is one of these kinds of tests. By deliberately trying to break, crash, or otherwise disrupt an application, fuzzing exposes its weaknesses and helps developers or security teams to determine where there needs to be a fix. It’s not a perfect tool; usually, fuzzing works best when combined with other types of testing. However, it is highly effective at detecting XSS, DDoS, and injection attacks.

Given the recent uptick in AI-driven technology, it should come as no surprise that some security professionals have begun including AI in their testing approaches. Although the traditional model has been successful in the past, AI brings a few very useful advantages to the table.

  • Improved specificity. Traditional fuzzing relies largely on random inputs. Systematically trying every possible input permutation would take far longer than any security team has to spend on one application. However, AI can be more focused. AI-driven tools can learn in real time what tactic or input is most effective, and it can adapt its strategy accordingly.
  • Because of its independence, AI-driven fuzzing can handle more applications in less time than a more manual solution. Implementing this type of fuzzing tool allows you to grow your suite of apps without sacrificing security.
  • Faster testing. Traditionally, security professionals would have to input all possible variables manually. With AI-driven fuzzing, the tool will generate its own inputs, saving substantial amounts of time.

Historically, fuzzing has been challenging to implement. However, an increase in AI-driven services and solutions has improved accessibility for organizations.

Implementing AI Fuzzing in CI/CD Pipelines

Fuzzing is effective, but it has also been more expensive than is sustainable for many companies. As a result, most developers have not taken advantage of it while building apps. Since fuzzing has not been a critical component of development for most organizations, they will need to begin integration with their existing testing frameworks to use it effectively.

One of these frameworks, the CI/CD pipeline, is a good place to start. This pipeline already helps increase security measures early in the software development lifecycle, and adding AI fuzzing will benefit the process. It streamlines software delivery by integrating software development and security testing, so fuzzing can be relatively straightforward to include as part of the automated testing.

Realistically, teams will never be done with security testing. There are always more bugs and vulnerabilities to find. However, including AI fuzzing in testing can help them find the bugs more quickly, which can in turn move software development along more quickly. Additionally, incorporating AI fuzzing into your CI/CD pipeline can make your testing frameworks more scalable and sustainable.

When your application is large and complex, testing all of it successfully is very challenging. Integrating AI fuzzing makes it faster and easier to comprehensively check the app for issues without overwhelming security and development teams.

Advanced AI Fuzzing Strategies

The most basic form of AI fuzzing can be helpful, but there are advanced strategies that can provide additional benefits to security testing frameworks. These include:

  • Genetic algorithms. Using genetic algorithms makes it possible to find the best solutions for an application. These algorithms start with a population of potential solutions, and AI can then whittle these down and recombine them until the optimal solutions or test cases are achieved. This can make the possible inputs generated for fuzzing more appropriate and effective for crashing the app.
  • Reinforcement learning algorithms. One possible way to implement machine learning is reinforcement. Through trial-and-error, this kind of algorithm maximizes rewards to encourage targeted vulnerability discovery.

AI-powered fuzzing is not a perfect solution. Even with AI, fuzzing can still be somewhat time-consuming and resource-intensive. However, fuzzing is a highly effective way to test applications for weaknesses and flaws, and as AI makes it more usable for many organizations, it may soon become invaluable. Especially when it is combined with advanced strategies and algorithms, fuzzing can help organizations stay ahead of attackers in the long run.