Picture this: An agent analyzes server responses, mapping the application's data flow and identifying patterns in user identifiers and access tokens. It begins testing and then: a breakthrough. The server responds, not with an error but with unauthorized data. If the agent could feel satisfaction, it would have a big smile on its face. It logs the vulnerability and refines its approach. The game has just begun.
Although there are many weaknesses and exploits, 73% of successful breaches in the corporate sector were carried out by penetrating web applications through their vulnerabilities. Such a high number of successful exploitations means that companies must step up their game when securing their web applications.
A successful attempt can often be a numbers game (the more attempts you make, the more chances you have), but it isn’t necessarily good. Attempting to brute force a system would create a lot of ‘noise,’ and the ratio of action to findings would be very low. Additionally, it might miss the non-obvious weaknesses. With that in mind, there needs to be a better way to test web apps than simply asking your team to attempt to penetrate your app repeatedly. Enter agentic pen testing.
Traditionally, pen testing is a systematic process used to identify, analyze, and mitigate vulnerabilities in IT systems and web applications. It can be done internally or outsourced to a vendor. To avoid disrupting operations and meet budgetary constraints, teams often perform pen testing at a single point in time, often once a year per application.
Considering the growing attack surfaces and the vast number of vulnerabilities popping up annually, this limited scope and frequency is a concern. In 2023, over 30,000 new vulnerabilities were published — a rate of a new vulnerability published every 17 minutes.
Even automated pen testing has its limitations. While these tools offer scalability and continuous testing, they often lack depth and business logic and generate excessive noise and false positives, especially in web applications. This means they struggle to uncover deep, context-specific, multi-step vulnerabilities, serving best as decision-support and compliance tools. This is where agentic AI can help. This technology can reason through attack paths, adjust in real-time based on responses, and solve complex security challenges.
Agentic AI pen testing uses AI agents to mimic adversaries, simulate real-world cyberattacks based on unique business risks. Unlike traditional testing methods, it is context-aware and adapts dynamically, making it a powerful asset for cybersecurity and DevOps teams.
At its core, AI-driven emulation enables agentic AI, at scale, to replicate human-like decision-making, interpret alerts, assess security gaps, and execute attacks accordingly.
Acting as a microservice for ethical hacking, it integrates within the control flow to determine the best course of action. Its dynamic and scalable design ensures that each agent performs a specific, autonomous task before seamlessly reintegrating into the larger workflow, making penetration testing more intelligent and efficient.
Each company has unique infrastructure, architecture, and risk factors. You can decide where to deploy agents and what to have them look for. Some examples are the client’s information DB, external API connections, and their scope in time and permissions. For the client’s information DB, for example, you would typically ask questions like:
Unlike human testers, you can always add agents and devise new workflows and rules to cover additional concerns.
Compliance with various framework requirements is one of the main reasons companies perform pen tests, even more than an actual desire to receive a report with actionable findings. Some common frameworks you should comply with (depending on industry and product type) include SOC 2, PCI DSS, ISO 27001, and GDPR. Almost all frameworks include stringent documentation requirements and comprehensive findings, results, and response logs. AI agents can be far more transparent than most testers and are far less likely to complain about the need to compose another boring report.
AI agents are still AI, so you shouldn’t trust them blindly, no matter how much you desire a frictionless pen-testing black box approach.
Since no one knows your exact infrastructure, architecture, and risk factors like your security people, they need to define the workflows, rules, and tasks the agents perform. A human should always be in the loop, reviewing the reports and approving the actions and tests. Some tests, like checking if an SQL DB can withstand a ‘drop database db_name’ command, should never be done.
AI agents are endlessly patient, tireless, and diligent. Whatever task you assign them, they’ll perform without complaint or don’t even need a lunch break. As such, AI agents can have unparalleled coverage, enabling them to check whatever you can come up with as a viable test. They’ll meticulously perform each task and transparently report every finding, lack of finding, and needed response. They can even include code snippets and screenshots if you request these.
Another advantage is speed. AI agents for pen testing are incalculably faster and more efficient than any human tester, which is part of what enables their coverage and quick response to changes.
Even considering all of that, there is a good chance that a testing tool comprised of AI agents will cost far less than a constant human red team or pen tester on retainer, especially a team required to do everything we’re asking the agents to do and to do it continuously. AI agents blend code's precision with human testers' adaptability, executing attacks, analyzing results, and adjusting strategies in real-time.
Overall, an agentic AI pen testing tool can be tailored to your needs faster, more cost-effective, and more efficient. So, how can you start implementing such a tool?
First, meet with security and development teams to evaluate your web application’s needs. Identify which areas need AI-driven penetration testing, such as authentication flows, API endpoints, or third-party integrations, and define the scope of automated vs. manual testing. Consider your app’s architecture, existing security controls, and compliance requirements to determine where AI-powered testing can provide the most value.
Once you know what workflows and rules you’d like to cover, you can either build an agentic AI system from scratch or choose a partner with such a solution built and ready to be custom-fitted.
If you decide to work with a partner, it's a good idea to start small with a single application as a testing ground for the solution’s capabilities. Once satisfied, you can expand the coverage. A few important things to consider include what could happen if this partner is hacked or compromised, where your information and credentials are saved and accessed, where they are being sent to, and what openings the tool creates into and out of your system.
You’d likely like to cover existing workflows to ensure they are constantly secure. You should integrate agents into existing workflows before creating new workflows whole-cloth as new test ideas occur. Secure and test the obvious workflows first before checking for obscure cases.
Preparing all relevant personnel to integrate the new solution is also essential. Your team would need to know how to interact with the AI agents, how to interpret the reports they are likely to get, and when to approve or escalate required actions following a vulnerability discovery. Reviewing your existing processes and workflows will help you tidy your playbook and ensure all your team members are on the same page regarding potential hacks, their location, their remediation process, and their results.
The benefits of agentic pen testing probably sound too good to be true, and you’re waiting for the other shoe to drop. Like with all AI tools, you should exercise caution, especially when cybersecurity is involved. AI can sometimes exhibit problematic or unexpected behavior. It is heavily influenced by its training data (GIGO), and bad data can cause unintentionally false or weird results.
When you test the solution you intend to integrate, search for hallucinations, prompt injection attacks, or unfiltered user-provided text interacting with agents directly. Ensure the tool has the proper guardrails, is testing itself and your systems, and has humans in the loop in all critical junctions.
So, you want to try this new pen testing tool - who will you call? Terra Security offers the first-ever agentic AI platform for web application pen-testing. It incorporates a human-in-the-loop mechanism out of the box and can perform thousands of best-of-breed tests for a complete attack surface coverage. It offers context-aware continuous testing and tailor-made attacks based on your unique business context and risk profile. Get a demo to see how their agentic-AI pen testing solution is poised to revolutionize the way enterprises build and run pen testing programs.
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