Security research and AI hype usually exist in separate worlds. Security practitioners are paid to be skeptical; AI vendors are incentivized to oversell. Project Glasswing — Cloudflare’s months-long experiment running security-focused LLMs against their own live infrastructure — is one of the most honest bridges between those worlds published to date.
Written by Grant Bourzikas, Cloudflare’s Chief Security Officer, the post is a 9-minute read that cuts through the usual AI-in-security narratives and gets into what actually happened when state-of-the-art models got pointed at real production code.
What Cloudflare Actually Did
The experiment wasn’t a controlled benchmark or a contrived demo environment. Cloudflare pointed a range of security-focused LLMs — including Mythos Preview, Anthropic’s security-specialized model — at more than fifty of their own production repositories.
The goal was dual-use: identify real vulnerabilities in their own systems before attackers do, and understand what capabilities these models would give attackers access to. “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,” Bourzikas writes.
Mythos Preview was the standout performer. Cloudflare was invited to test it as part of Anthropic’s Glasswing program specifically because of Mythos’s security focus.
What Mythos Actually Found
Bourzikas is unambiguous about the step-change Mythos Preview represents: “The jump from what was possible with previous general-purpose frontier models to what Mythos Preview does today is not just a refinement.” He calls it “a real step forward” before getting into the limitations — which is the right framing. General-purpose frontier models applied to security code review were producing a lot of noise and a few real finds. Mythos changed that ratio materially.
The specific performance details in the post illuminate the practical gap between benchmark claims and operational reality:
- Pattern recognition — Mythos excels at identifying known vulnerability classes, common anti-patterns, and security misconfigurations across large codebases quickly
- Context retention — The model maintains relevant context across multi-file analysis, which is critical for finding vulnerabilities that span service boundaries
- Explanation quality — The model’s ability to explain why something is a vulnerability (not just flag it) proved critical for triaging and prioritizing findings
Where Models Still Fall Short
Honesty about limitations is what makes the Glasswing post worth your time. Bourzikas identifies several areas where current models — including Mythos — still require significant human scaffolding:
Novel vulnerability classes. Models trained on historical vulnerability patterns struggle with genuinely novel attack vectors. If the vulnerability class doesn’t exist in training data, detection rates drop significantly.
Authorization and business logic. Cryptographic and memory-safety bugs are pattern-matchable. Access control violations that depend on understanding the intent of a system — not just its code — remain hard. A model can see that an endpoint accepts a parameter; it can’t always reason about whether that parameter should be accessible to that caller in that business context.
False positive management at scale. Against fifty production repositories, the volume of potential findings is enormous. Current models don’t yet have reliable enough precision to enable fully autonomous triage — everything still requires human review to separate signal from noise.
The Architecture Lesson
The most practically useful section of the post is the operational framing: what the scaffolding around these models needs to look like before any of this can scale.
Bourzikas describes a layered approach:
- Models run in isolated sandboxed environments with read access to code but no execution capability
- Human security engineers review and triage all findings before any remediation action
- Findings are fed back into a continuous pipeline rather than run as one-off scans
- The output feeds a vulnerability tracking system, not just a report
The implicit lesson is that the model is not the product. The operational workflow around the model — how findings are surfaced, reviewed, prioritized, and tracked — is what determines whether AI-assisted security review produces real outcomes or expensive noise.
Why This Matters for Agentic AI Broadly
The Glasswing experiment is a microcosm of a broader challenge in agentic AI deployment: the gap between demo capability and production capability. In a demo, an AI agent finds a critical vulnerability in a repository and the audience is impressed. In production, the same agent runs against fifty repositories and produces 3,000 findings, 87% of which turn out to be false positives, and the security team is now more overwhelmed than before.
The teams closing that gap fastest — like Cloudflare appears to be doing — are the ones that treat the model as a component in a larger operational system, not as a standalone solution.
The fact that Cloudflare is running this experiment on their own infrastructure — not a customer’s — also matters. Eating your own dog food with security AI tools is the highest-integrity test available. If it breaks Cloudflare’s infrastructure or produces bad results, the incentive to report accurately is strong.
Sources
- Cloudflare Blog — “Project Glasswing: what Mythos showed us” by Grant Bourzikas, May 18, 2026
- Anthropic — Project Glasswing program
Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260518-2000
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