The Experiment: Are AI Penetration Testing Tools as Good as Human-Led Ones?
Over the past six months, we evaluated several AI penetration testing tools and services to better understand their current capabilities, consumer value, and potential role within a security program.
We analyzed a variety of solutions, ranging from open-source tools to commercial-grade products.
The goal of this review was to determine whether these tools could reasonably replace a human-led penetration test, but also to assess how effectively they could support human-led penetration testing and produce meaningful findings.
I’ll cut to the chase: the results were underwhelming.
A common theme in our findings is that solutions position themselves as replacements for human-led penetration testing, and we have seen significant adoption among companies seeking to benefit from the cost savings and quality these solutions claim to offer.
While many of the tools presented strong marketing claims around autonomous testing, intelligent attack path discovery, vulnerability identification, and workflow acceleration, their practical performance didn’t consistently match those claims.
In most cases, the tools were able to perform basic reconnaissance, enumerate common services, generate somewhat useful observations, or suggest standard testing approaches.
However, they struggled to move beyond surface-level analysis and rarely produced findings that would be considered novel or high-impact.
A recurring limitation was the lack of meaningful findings. The tools frequently identified low-value issues, repeated information already available from basic scanning utilities, or produced broad recommendations that lacked sufficient technical depth.
Human-Led Pen Testing vs. AI Tool Findings: The Results Are In!
Below you’ll find an aggregate of FRSecure’s human-led pen test findings vs. the total findings of the AI penetration testing tools.
Across all tests, AI products uncovered exactly one finding that one of our human testers missed—a ‘low severity’ finding related to a DNS setting.
At a glance, it can look like the AI penetration testing uncovered more than our testers. However, the severity level was often misinterpreted (see Test 2 above, where AI tools uncovered 11 low-severity findings compared to our three. But several of these low-risk findings were misclassified and should have been identified as medium-severity, which was accounted for in our reporting).
In total, there were 405 findings from humans and 98 from AI solutions.
The lack of findings here is a false negative, giving the person reviewing the data a false sense of security about their environment. The issues are still present; you just wouldn’t know they’re there.
AI Penetration Tests vs. Commercial Vulnerability Scanners
Even more damning, the results show that the AI products were regularly outperformed by commercial vulnerability scanners.
Why is this?
Cost.
Commercial vulnerability scanners have been around for decades and have become very good at what they do.
The tools that almost every AI solution uses for testing are free and open source.
We love open-source tools at FRSecure and use many of them in the penetration tests we perform. However, for general vulnerability scanning, they often fall short of commercial scanners.
Oh, and is AI Penetration Testing Really Even AI?!
After digging into the technology behind some of these AI penetration testing tools, it became clear almost immediately that the engine driving these systems is the high-level orchestration of “if, then” statements.
This isn’t necessarily bad.
Autonomous penetration testing tools have been around for years and can serve a purpose in a mature security program. The problem is that when solutions put “AI” in their name, it implies a level of intelligence in their product that the consumer simply isn’t getting.
In some instances, AI or large language models (LLMs) are being leveraged to add business risk intelligence to the findings being produced.
The problem with this is that business risk is one area where you need a human more than almost any other.
Through discussions with stakeholders and an understanding of how the organization operates, human testers can recognize how seemingly minor vulnerabilities may have significant business consequences, or why technically critical findings may actually present limited risk.
While AI tools can assess technical severity, they generally lack the organizational context, situational awareness, and professional judgment necessary to accurately determine meaningful business risk.
A Positive: AI Has a Supporting Role in Penetration Testing
All that said, the evaluation identified some areas where these tools may provide value.
AI penetration testing tools can help with things like:
- Accelerating repetitive tasks
- Organizing notes
- Suggesting test cases
- Summarizing scan data
- Supporting less-experienced analysts with guided workflows
They may also be useful for initial triage, checklist generation, report drafting, or helping analysts think through possible next steps during an assessment.
In this supporting role, the technology has some promise.
The Verdict: AI Penetration Testing Isn’t Ready to Replace Human Testers
In conclusion, these tools should not be viewed as a replacement for skilled penetration testers or as a reliable source of independent findings.
We should use these products the same way autonomous penetration testing platforms have been used to date—for continuous testing as a stopgap between traditional penetration tests and as a tool to assist skilled analysts.
The market will keep evolving, and we’ll keep watching it closely. But for now, nothing replaces a real human finding what a real attacker would.
If you want penetration testing backed by actual human-led expertise, see how FRSecure’s services get it right.
