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Fortune Exclusive: AI cybersecurity startup RunSybil, founded by OpenAI’s first security hire, raises $40 million led by Khosla Ventures

AI Hacking
March 18, 2026
by
Press
AI Hacking
March 18, 2026

Fortune Exclusive: AI cybersecurity startup RunSybil, founded by OpenAI’s first security hire, raises $40 million led by Khosla Ventures

Featured in Wired! I Watched AI Agents Try to Hack My Vibe-Coded Website

AI Hacking
March 18, 2026
by
Press
AI Hacking
March 18, 2026

Featured in Wired! I Watched AI Agents Try to Hack My Vibe-Coded Website

RunSybil, a startup founded by OpenAI’s first security researcher, deploys agents that probe websites for vulnerabilities—part of a new AI era for cybersecurity.

Featured in Wired! AI’s Hacking Skills Are Approaching an ‘Inflection Point’

AI Hacking
March 18, 2026
by
Press
AI Hacking
March 18, 2026

Featured in Wired! AI’s Hacking Skills Are Approaching an ‘Inflection Point’

AI models are getting so good at finding vulnerabilities that some experts say the tech industry might need to rethink how software is built.

Agentic AI, Automation & Cyber Power at Munich Cybersecurity Conference

AI Hacking
March 13, 2026
by
Sybil
AI Hacking
March 13, 2026

Agentic AI, Automation & Cyber Power at Munich Cybersecurity Conference

Beyond Introspection: The Apollo Federation Attack Surface Hidden in Plain Sight

January 14, 2026
by
Sybil
January 14, 2026

Beyond Introspection: The Apollo Federation Attack Surface Hidden in Plain Sight

An exposed Apollo Federation subgraph can leak its full schema and internal graph behavior through federation helper fields, such as _service { sdl }, even when standard GraphQL introspection is disabled. This is easy to miss because it sits outside common GraphQL attack patterns, is enabled by default, and is only briefly documented. So most testers never think to look for it. The result is a collapsed trust boundary where attackers can enumerate schemas, mimick the router, and access internal entity data. Sybil uncovered this by systematically exploring framework-level behavior rather than relying on known vulnerabilities or assumptions about what should be “internal.”

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