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Rubrik said it overhauled its internal code review and vulnerability triage pipeline after gaining access to Anthropic’s Mythos Preview through Project Glasswing and finding that the model could identify complex vulnerability chains across large codebases faster than the company’s existing security workflows could process them. The volume of findings created an immediate bottleneck in prioritization and remediation, prompting Rubrik to abandon plans to simply add more human reviewers.
Instead, the company built an automation-focused harness around the model to inject business and security context, manage tool calls and checkpoints, and iteratively narrow broad results into prioritized issues for engineers. Rubrik said it limited automated remediation to narrowly defined vulnerability classes where machine-generated fixes were reliable, while routing critical or less-certain findings to human engineers, reflecting a broader challenge seen in Glasswing deployments, where partners have reportedly identified more than 10,000 high- or critical-severity vulnerabilities but still struggle to patch them across sectors including technology, power, water, and healthcare.
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Anthropic expanded Project Glasswing in June to roughly 150 organizations across 15 countries. Access to Mythos Preview was limited to vetted partners invited into the program.
Rubrik decided not to fully automate vulnerability remediation and restricted automated fixes to a tightly scoped set of predefined vulnerability classes. Findings outside those classes were routed to engineers for human judgment and final remediation.
Rubrik built a software harness around Mythos to manage tool calls, checkpoints, and added business and security context, effectively rebuilding its code review pipeline. It also used iterative targeted scanning passes to reduce noise and route higher-priority findings to engineers.
Faced with the volume of findings, Rubrik initially considered adding more human reviewers but concluded that human-driven remediation could not keep pace with AI-speed discovery. The company chose to overhaul its process instead of primarily expanding manual review capacity.
After joining the program, Rubrik assembled a dedicated multi-functional engineering and infosec team to handle threat discovery and remediation around Mythos. The team was tasked with building supporting processes rather than relying on the model alone.
Using Mythos Preview, Rubrik scanned its own codebase and identified complex vulnerability chains and a large volume of potential security issues that its usual tools and methods had missed. The initial use included whole-repository scanning before more targeted passes.
Rubrik joined Anthropic's Project Glasswing and received access to the Mythos Preview model for early use. The company then began applying the model to its own software security workflows.
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AI-assisted vulnerability research is contributing to a sharp rise in disclosed high- and critical-severity software flaws, with monthly totals reportedly climbing from fewer than 100 to more than 600. Anthropic's Project Glasswing reportedly identified over 10,000 high- or critical-severity issues, while exploitation of disclosed vulnerabilities is increasingly occurring on the day of disclosure or earlier, making patch cycles measured in weeks inadequate. Okta used pre-release OpenAI and Anthropic frontier models through the Daybreak Cyber Partner Program and Project Glasswing to scan high-priority customer-installed software across hundreds of repositories. Its testing found that autonomous agents frequently generated false positives or overstated severity, while expert-led investigation, manual reproduction, and triage substantially improved results. VMware similarly prioritized fixes found after adopting Claude Mythos and Project Glasswing, releasing VCF 9.1.1 as a security-focused update and delaying planned VCF 9.2 features.
Sep 9, 2026
Anthropic unveiled **Claude Mythos Preview**, an unreleased AI model it says can autonomously discover and exploit severe software vulnerabilities across major operating systems, browsers, open-source projects, and some closed-source targets. The company said the model uncovered thousands of high-severity flaws, including long-lived bugs in **OpenBSD**, **FFmpeg**, Linux kernel privilege-escalation chains, and **`CVE-2026-4747`**, a FreeBSD NFS remote code execution flaw that could enable unauthenticated root access. Anthropic withheld broad release, citing offensive cyber risk, and instead launched **Project Glasswing**, a gated program for roughly 40 to 50 partners such as AWS, Apple, Cisco, Cloudflare, Google, JPMorgan Chase, Microsoft, Mozilla, NVIDIA, and Palo Alto Networks to validate findings, patch affected software, and study defensive uses. Independent and industry assessments broadly agreed Mythos marks a significant advance in AI-enabled cyber capability, though several researchers questioned how much of Anthropic’s headline claims can yet be verified through public CVEs and warned that similar results may be reproducible with cheaper or open models plus strong tooling. The UK AI Security Institute found Mythos achieved a **73%** success rate on expert capture-the-flag tasks and completed a full 32-step simulated enterprise attack in 3 of 10 runs, while Anthropic later reported coordinated disclosure activity spanning **1,596 vulnerabilities across 281 open-source projects** and partners identifying more than **10,000** high- or critical-severity candidates. Governments, financial regulators, and CISO groups in the US, UK, Europe, Canada, and Japan responded with briefings and warnings that AI is compressing the gap between vulnerability discovery and weaponization, leaving remediation, patch governance, and defensive automation as the main bottlenecks.
Aug 31, 2026
Anthropic's Project Glasswing reportedly generated **26,153** open-source vulnerability findings, but public disclosure records show a far smaller number progressing through validation and remediation. Public coordinated-vulnerability-disclosure cards across eight projects document 12 CVEs and five GHSAs—including three critical and seven high-severity cards—while attributing the accepted work to Glasswing without identifying a specific Anthropic model in most cases. Only `CVE-2026-4747` has separate primary-source attribution to Mythos. The disparity illustrates that AI-assisted discovery can outpace human triage, patch authoring, maintainer review, and enterprise deployment. Anthropic's public ledgers reportedly reflected about 202 fixed vulnerabilities after five months, while Trail of Bits and OpenAI's Patch the Planet found 1,646 issues in 59 codebases but had only 215 merged patches as of September 4. Organizations should expect larger vulnerability queues and prioritize remediation using evidence of exploitation, asset reachability, compensating controls, and business exposure rather than CVSS scores alone.
Sep 16, 2026