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“Too dangerous to release” is a bold claim in cybersecurity, so we treat it like any other security headline: we interrogate it. We kick off our monthly news round-up by welcoming Catherine McNamara as a permanent co-host, then dig into Anthropic’s Mythos preview model and Project Glasswing, positioned as an AI security and threat intelligence leap that can allegedly find zero-day vulnerabilities at a level the public shouldn’t have yet. We ask the uncomfortable questions: where’s the independent evidence, what does high-fidelity vulnerability discovery actually look like, and how do we avoid drowning in AI-generated noise?

From there, the discussion gets messier in the way real security always is. We talk about tokens, paid code security reviews, and how incentives change when AI companies chase growth, IPO pressure, and government contracts. We also unpack why “ethical” restrictions are hard to enforce in practice and how rumors of source code leaks and fast rewrites complicate any promise of controlled access. If powerful agencies can use AI to speed up exploit discovery, even lower-severity bugs can become dangerous when chained into real attacks.

Then we pivot to a concrete lesson every org can use: the Vercel breach. A supply chain compromise plus a single OAuth “Allow All” moment shows how identity and SaaS permissions failures can open the door to data exfiltration. We break down least privilege, blocking risky OAuth grants, shadow SaaS, and why a CASB can be the difference between a contained incident and a headline.

We close by connecting AI layoffs to social and economic pressure, including CEO security fears, surprising UBI rhetoric, and Oracle laying off 30,000 people by email. If you care about AI, cloud security, appsec, and what these incentives are doing to the world, this one’s for you. Subscribe, share the episode with a friend, and leave a review with your take: is the AI security boom helping defenders more than attackers?

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