Autonomous AI-Powered Penetration Testing Platform

XBOW develops autonomous AI systems designed to augment offensive security operations by simulating advanced attacker behaviors. Its platform acts as a self-operating penetration tester capable of independently identifying vulnerabilities such as XSS flaws, SQL injections, and authentication bypasses across web applications and infrastructure components like VPN portals.

The system employs machine learning models trained on extensive datasets to replicate human-like decision-making processes, including context-aware adaptations when encountering novel defenses or architectural complexities. For targeted assessments, XBOW combines systematic parameter fuzzing with deeper structural analyses of clientside resources like JavaScript libraries and API endpoints to pinpoint injection vectors often missed by static scanners.

Enterprise implementations integrate seamlessly into SDLC workflows, providing actionable reports aligned with CVSS severity metrics while minimizing false positives through rigorous validation protocols. The platform has demonstrated successes across critical infrastructure products and collaborates with bug bounty programs to help organizations benchmark defensive postures against evolving adversarial tactics.

XBOW received $75 million in a Series B in June 2025. The round was led by Apoorv Agrawal of Altimeter with participation from two previous investors: Sequoia Capital and Nat Friedman. Total funding now stands at $117 million.