Why Automated Pentesting Feels Broken (and What We’re Building About It)

Hi everyone 👋 

For a long time, the cybersecurity community has been stuck in a cycle that feels increasingly broken. We have incredible tools for Web App and API pentesting that can scan thousands of endpoints in minutes, yet security teams are more overwhelmed than ever.

The problem isn't a lack of data; it’s a noise problem.

If you’ve spent any time in AppSec, you know the drill: you run a scan, and you’re handed a massive list of "potential" vulnerabilities. Most of these turn out to be theoretical risks or outright false positives.

So teams end up stuck between two imperfect options:
 Automated security tools that are fast but noisy, or manual pentesting that is deep but impossible to scale.

 
As we looked at how the ecosystem works today, we realized that while AI is changing how we write code, security validation hasn't really caught up. We’re still largely relying on signature-based automation—static patterns that can't "think" or adapt to how an application actually behaves.

This is why we’ve been working on something different. We wanted to move away from "cosmetic AI" that just summarizes reports and toward Agentic AI that can actually reason like an attacker.

In our view, an AI agent shouldn't just find a bug; it should be able to prove it. It needs to look at a complex auth flow or a multi-step business logic sequence and understand how to "pull the thread" to see if it actually leads to a breach. 

We’re getting ready to launch ZeroThreat Agentic AI Pentesting very soon. It’s designed to extend our existing Web and API testing by adding a layer of decision-driven execution. It’s not just about finding vulnerabilities; it’s about validating real exploit paths through autonomous reasoning.
 

We’re moving from "Detection" to "Exploitation," and we’d love to know: what’s the one thing that frustrates you most about the vulnerability reports you’re getting today?

 
Stay tuned—we’re just getting started.