We’ve all been there—staring at a terminal for hours, scrolling through endless subdomains, and drowning in noise. As a hunter, the biggest bottleneck isn’t finding the target; it’s the sheer volume of data you have to sift through to find the real vulnerabilities.
I decided to stop fighting the noise and start automating the intelligence. I’ve been building out an AI-assisted pipeline that doesn't just scan—it reasons through an attack surface.
My Current Pipeline
Recon & Mapping: Using tools like ParamSpider and Shodan to feed data into my local environment.
The AI Layer: I’m running local Ollama models integrated into a Termux Ubuntu environment to act as my triage agent.
Workflow Orchestration: By utilizing OpenClaw and Cloudflare Workers, I’ve built an offensive security pipeline that runs autonomously.
The Big Lesson
An AI is only as good as the context you give it. The hardest part wasn't the code; it was managing the false positives. By refining my prompts and building a "second-pass" validation agent, I’ve significantly cut down my manual triage time.
I’m still iterating on this pipeline daily. How are you integrating LLMs into your security workflows? Are you using agents for triage, or are you still sticking to manual analysis?
Drop a comment—let’s talk about the future of AI-assisted security.
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