AI Pentesting Tools vs Automated Vulnerability Scanners

AI pentesting and automated vulnerability scanners both hunt down and report on security vulnerabilities in your system. The key difference between AI tools vs. vulnerability scanners is that the scanners are limited to known vulnerabilities and lack the business context to identify a real threat from noise. AI pentesting validates whether a vulnerability is truly exploitable, delivering highly accurate, actionable results.

Key takeaways

• Automated vulnerability scanners are security testing tools that scan your systems for known vulnerabilities.
• AI pentesting similarly looks for vulnerabilities in your system, but also validates whether they are truly exploitable.
• AI pentesting is more accurate because it only reports vulnerabilities that present a real risk. In this way, it dramatically reduces noise and false positives.
• XBOW is leading the shift from manual to AI-led autonomous pentesting. Its solution is finding real, exploitable vulnerabilities at machine speed, and without false positives.

What are automated vulnerability scanners?

Automated vulnerability scanners are tools that scan your systems – from applications to cloud or network – for patterns of known vulnerabilities. For instance, code or application scanners scan source code for known vulnerabilities like SQL injection or cross-site scripting.

Automated scanning limitations

Since vulnerability scanners only identify known vulnerabilities without weaving in any bigger context like business logic or priorities, they are prone to false positives. For instance, they may highlight a vulnerability in an application that has no PII and is not Internet-facing, meaning there’s almost no chance of exploitation. In addition, they can’t identify most business logic flaws, which don’t follow known patterns, or zero-day vulnerabilities, which also don’t feature previously identified patterns.

What is AI pentesting?

In penetration testing or pentesting, a skilled human pentester thinks like a hacker and attempts to breach a system, in turn highlighting vulnerabilities that are proven exploitable. This form of testing is the gold standard of security assessments due to its high accuracy and delivery of real, exploitable issues, not just potential ones. AI pentesting features the same stages and activities as traditional pentesting, but AI plays a role throughout – from reconnaissance to reporting. Ultimately, AI acts as a force multiplier for pentesting, allowing teams to do more of it at a lower cost.

Shortcomings of AI pentesting

Humans still play a critical role in pentesting, but AI takes the low-level, mundane tasks off their plates. However, there are some AI vulnerability coverage gaps. The limitations of AI pentesting include:

• Certain types of business logic and context, including risk tolerance, regulatory nuances, business impact severity. lthough AI is rapidly improving its ability to understand and apply business logic and context to testing results, there are always additional layers of advanced human logic that can be applied.
• Humans needed for scoping.
• Some regulations, like PCI, require human review.
• Novel architecture edge cases:highly custom environments may require human reasoning.

AI pentesting vs. scanners

The key difference between AI pentesting and scanners is accuracy. Vulnerability scanners highlight vulnerabilities that could, in theory, lead to an exploit. AI pentesting highlights vulnerabilities that are proven exploitable. Pentesting has always been a more effective and trusted method of security testing, but due to its heavy reliance on humans, had a coverage problem. It simply wasn’t cost effective to regularly employ it across the entire attack surface, leaving organizations exposed. But with AI in the mix, pentesting at scale becomes possible. And it leaves the human pentesters to do what they do best, while it handles the menial tasks like identifying known vulnerabilities or drafting reports.

Example of differences between automated vulnerability scanners and AI pentesting

To illustrate the difference between automated vulnerability scanners and AI pentesting, consider dynamic application security testing or DAST. At first glance, these two types of security testing seem very similar – they both assess the security of running applications. But the similarities end there. The differences between these two types of security testing include:

Noise

DAST scanners can be very noisy. There are a lot of results, and a lot of false positives. DAST is weak at vulnerability validation, and often generates low-quality, informational findings, like a server header that discloses the server version. AI pentesting is far more accurate because it only returns results that are proven exploitable.

Business logic

DAST scanners have no (or a poor) ability to identify business logic vulnerabilities like IDOR (insecure direct object reference) or broken object level authorization (BOLA). These vulnerabilities that allow things like privilege escalation and improper account access are challenging for DAST. It lacks the ability to distinguish between a guest page and an administrator page and to determine which users can access them. AI pentesting can identify business logic vulnerabilities, and could, for instance, look at a page and determine if it contains sensitive information and if its current user role should be allowed to view it.

Fill in the gaps left by your automated vulnerability scanners with XBOW

The XBOW autonomous offensive security platform finds vulnerabilities in your environment, and then proves they are exploitable before reporting them. Get a quick demo to see its accuracy first hand and compare what it finds to what your automated vulnerability scanners do.

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This post is part of the XBOW Offensive Security Academy, an educational blog series that discusses and explores offensive security tactics and techniques in the age of AI.