According to a detailed report from Rubrik’s CTO, the company’s use of Anthropic PBC’s Mythos Preview model revealed a significant volume of potential security flaws, prompting a strategic shift in how remediation is handled. Rubrik’s experience highlights the capabilities and challenges of applying advanced AI-driven vulnerability scanning in high-trust production environments.
- Scanned entire code repositories using Mythos leading to many flagged vulnerabilities
- Rebuilt remediation pipeline emphasizing trustworthy automation over volume
- $100M API credits supported wide Mythos access across various industries
Product angle
The source review details Rubrik’s experience integrating Anthropic’s Mythos Preview model to scan its software for vulnerabilities. Mythos, designed to uncover complex security flaws and chain them into exploit paths, triggered the company to reassess its remediation approach. Rubrik created a custom software harness around the AI model to manage inputs, incorporate business and security contexts, and enforce strict architectural boundaries, recognizing that prompt-based outputs alone could drift and produce unreliable results.
This hands-on experiment with Mythos demonstrates the potential and limits of AI-powered code scanning. While Mythos detected many issues, Rubrik’s team spent substantial engineering effort deciding which vulnerability classes were safe to automate fixes for, and which required human ownership. This experience exemplifies a balance between automating trustworthy parts of security workflows while retaining expert oversight.
Best for / avoid if
According to Rubrik’s findings, Mythos Preview and similar AI vulnerability scanners are best suited for organizations with mature software security teams that can contextualize and validate AI-generated findings. Enterprises focused on high-trust applications, where remediation mistakes could incur critical risk, will benefit from Mythos’s detailed analysis combined with a conservative approach to automation.
Conversely, organizations with limited security resources or that expect AI tools to fully automate vulnerability remediation might find Mythos less fitting without significant investment in integration and engineering. The model’s high detection volume and complexity require a disciplined process and selective automation to deliver value without overwhelming teams.
Pricing and alternatives to check
While specific pricing for Anthropic’s Mythos Preview was not disclosed, the wider program is supported by a substantial $100 million investment in API credits, enabling broad early access to partners across sectors including technology, critical infrastructure, and international agencies. This indicates that Mythos is currently positioned as an enterprise-level, invite-only solution rather than a mass-market product.
Alternatives to consider may include established code scanning and vulnerability management platforms that integrate AI components with mature ecosystems and wider availability. Enterprises should weigh Mythos’s pioneering capabilities against offerings from industry leaders or open-source tools depending on their scale, security posture, and automation goals.