WhatAn automated security testing platform that integrates into CI/CD pipelines, using multiple LLM agents to continuously probe applications for vulnerabilities before deployment.
SignalThe author spent $1,500 manually orchestrating LLM attacks against a vulnerable app, revealing that LLMs can genuinely find real security flaws — but the process is manual, expensive, and inconsistent across models, suggesting a productized version would save security teams enormous effort.
Why NowFrontier LLMs have just crossed the capability threshold where they can autonomously discover and exploit real web application vulnerabilities, making automated AI-driven pentesting viable for the first time.
MarketSecurity teams at mid-to-large companies paying $50K-$500K/year for manual pentests; TAM overlaps with the $5B+ application security testing market; competes with Snyk, Veracode, and traditional pentest firms but offers continuous AI-driven testing rather than point-in-time audits.
MoatAccumulating a proprietary dataset of vulnerability patterns, exploit chains, and model-specific prompting strategies across thousands of real-world applications creates compounding accuracy advantages.
I built a vulnerable app and spent $1,500 seeing if LLMs could hack itView discussion ↗ · Article ↗ · 393 pts · June 4, 2026
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