Project Glasswing: what Mythos showed us
**TL;DR:** Project Glasswing: what Mythos showed us
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What we know
For the last few months, we've been testing a range of security-focused LLMs on our own infrastructure. These LLMs help identify potential vulnerabilities in our own systems, so we can fix them – and they also show us what attackers are going to be able to do with the latest models. None of these LLMs has captured more attention than Mythos Preview, from Anthropic. A few weeks ago, we were invited to use Mythos Preview as part of Project Glasswing .
We soon pointed it at more than fifty of our own repositories – to see what it would find, and to see how it works. This post shares what we observed, what the models did well and what they didn't, and how the architecture and process around them needs to change, so they can be used at scale. What changed with Mythos Preview Mythos Preview is a real step forward, and it's worth saying that plainly before getting into anything else.
We've been running models against our code for a while now, and the jump from what was possible with previous general-purpose frontier models to what Mythos Preview does today is not just a refinement of what came before. It's a different kind of tool doing a different kind of work, and that makes a clean app
Source: Cloudflare Blog
Context
Tech news is rarely just a gadget headline. We frame what changed, who benefits, and what to watch next as details firm up.
Why this matters
The immediate headline is only the entry point. The more useful question is who gains leverage, who faces new risk, and whether the change is durable or experimental.
What to watch next
Follow whether independent researchers or regulators validate the claims — that is often when the real scope becomes clear.
Practical takeaways
1) If money or security is involved, wait for primary sources. 2) Test changes on a small scale before committing. 3) Note what would falsify your current assumptions.
FAQ
**Q: Is everything in this article confirmed?** A: The summary reflects publicly reported information at publication time. Analysis sections are clearly framed as context, not new reporting.
**Q: Will iByte update this page?** A: Yes. As primary sources publish more detail, this article can be refreshed without changing the URL.
Last updated: June 16, 2026.
Additional context: early-cycle stories often look bigger in headlines than in day-to-day impact. The useful move is to identify the smallest set of facts that would change your decision, then wait for those facts to land.