Pentesting

ProjectDiscovery Neo is an autonomous AI security engineer that runs real exploit chains, not just detection passes. In a three-application benchmark spanning banking, healthcare, and insurance targets, Neo confirmed 66 exploitable vulnerabilities — the highest count of any tool tested — including 24 findings that no other scanner or agent caught. What Is ProjectDiscovery Neo? (The Nuclei-Powered AI Security Engineer) ProjectDiscovery Neo is an autonomous penetration testing platform built on the Nuclei toolchain, designed to behave like a senior security engineer: it plans attack chains, executes exploits, validates impact, and returns proof packs that your team can replay. Unlike traditional scanners that flag potential issues, Neo confirms whether a vulnerability is actually exploitable before reporting it. The platform launched commercially at RSAC 2026 in March after ProjectDiscovery won the RSAC 2025 Innovation Sandbox — the highest-profile pre-launch validation any AI security startup has received. Underneath Neo sits Nuclei, the open-source engine that has completed over 10 billion scans and is maintained by a community of 100,000+ security engineers with 9,000+ YAML templates covering CVEs, misconfigurations, and custom attack patterns. Neo takes this attack-pattern library — which no new AI security startup can replicate overnight — and wraps it inside an agentic loop powered by Claude Opus 4.5, running 30+ agent-native security tools inside isolated sandboxes. The result is a tool that combines breadth (every CVE template Nuclei ships) with depth (multi-step reasoning to chain vulnerabilities into working exploits). ...

RunSybil is an AI-native offensive security platform that autonomously chains IAM misconfigurations, container escapes, and CI/CD secret exposures into full attack paths — operating black-box against live cloud environments the same way a real attacker would, with no source code or agent credentials required. What Is RunSybil? The AI-Native Pentesting Platform Explained RunSybil is an AI-native penetration testing platform founded in 2023 by Ari Herbert-Voss — OpenAI’s first security research hire — and Vlad Ionescu, formerly of Meta’s Red Team X. The company raised $40M in a Series A in March 2026, backed by Khosla Ventures, the Anthropic Anthology Fund, Menlo Ventures, Conviction, and Elad Gil, with angels from OpenAI, Palo Alto Networks, Stripe, and Google. The product centers on an autonomous AI agent called Sybil that operates against live cloud environments in pure black-box mode — no source code, no privileged credentials, no static playbook. Sybil observes what access it can gain, adapts its attack path accordingly, and chains multiple vulnerability classes together the way an actual human attacker would. This is a fundamentally different model from legacy automated scanners that run pre-defined scripts or check configuration against a compliance checklist. The platform specifically targets the attack surface that dominates modern cloud breaches: IAM misconfiguration, non-human identities (NHIs), container workloads, and CI/CD pipeline secrets — the four categories that together account for over 80% of cloud security incidents in 2026. ...