According to AnthropicAI on X, Anthropic introduced Project Glasswing, an initiative to secure critical software using its newest frontier model, Claude Mythos Preview, which can find software vulnerabilities at a level surpassed only by the most skilled humans (as reported by Anthropic). According to Anthropic’s announcement page, Glasswing focuses on high-impact targets like critical infrastructure, open source foundations, and widely deployed libraries, pairing automated vulnerability discovery with responsible disclosure workflows (according to Anthropic). For security teams, this signals near-term business opportunities in automated code review, red teaming, SBOM risk triage, and continuous dependency scanning powered by large reasoning models, while vendors can integrate Mythos-driven scanners into CI pipelines for earlier defect detection and reduced remediation costs (as reported by Anthropic).

According to Boris Cherny on X, Anthropic is responsibly previewing its new frontier model Claude Mythos Preview with cyber defenders instead of a broad release, citing the model’s powerful and potentially dangerous capabilities. As reported by Anthropic, Project Glasswing uses Mythos to identify software vulnerabilities at a level rivaling all but the most skilled humans, creating immediate opportunities for security vendors to accelerate code auditing, SBOM validation, and CI pipeline scanning. According to Anthropic’s model card, the preview is gated for high-trust partners, signaling an enterprise go-to-market focused on regulated sectors and critical infrastructure, while mitigating dual-use risks. As reported by Anthropic, organizations can integrate Mythos into red-teaming workflows and vulnerability triage to reduce mean time to remediation and prioritize exploitability, with defenders gaining earlier detection across large codebases.

According to Anthropic, Mythos Preview has already identified thousands of high-severity vulnerabilities across every major operating system and web browser, indicating strong potential for AI-driven vulnerability discovery at scale (as posted by Anthropic on X). According to the original Anthropic post, the preview results suggest coverage across mainstream OS and browser stacks, highlighting immediate enterprise security use cases for automated triage and prioritization. As reported by Anthropic on X, organizations could leverage AI-assisted code and binary analysis pipelines with Mythos-like models to reduce mean time to detect and remediate critical issues in software supply chains. According to Anthropic’s announcement, the scope across OS and browsers implies commercial opportunities for managed vulnerability discovery services, continuous scanning integrations with CI/CD, and partnerships with security vendors focused on patch orchestration and risk analytics.

According to @AnthropicAI on X, Anthropic introduced Project Glasswing, an urgent initiative to secure critical software using its newest frontier model, Claude Mythos Preview, which it claims can find software vulnerabilities better than all but the most skilled humans. As reported by Anthropic’s official announcement on X, the program targets high-impact codebases where rapid, automated vulnerability discovery can reduce risk and remediation time. According to Anthropic’s post, the Claude Mythos Preview model is positioned for offensive security analysis tasks such as code review, exploit pattern detection, and triage support, indicating near-expert performance on vulnerability discovery. For security buyers and dev teams, this implies faster secure SDLC integrations, earlier defect detection, and potential cost savings across penetration testing cycles, according to Anthropic’s stated capabilities on X.

According to Ethan Mollick on Twitter, an independent extension of METR’s time-horizon analysis applied to offensive cybersecurity finds a 5.7-month capability doubling time, with frontier models achieving 50% success on tasks that take human experts 10.5 hours. As reported by Ethan Mollick, this mirrors METR’s published timelines and uses real human expert timing data, indicating rapid progress in automated vulnerability discovery and exploitation. According to Ethan Mollick, these findings imply accelerating ROI for red teaming, SOC automation, and pentest augmentation tools, while raising urgent needs for defensive AI investments such as automated patch prioritization and continuous adversarial simulation. As reported by Ethan Mollick, vendors can productize model-in-the-loop workflows for exploit development triage, while enterprises should update risk models and procurement to account for sub-year model capability doubling.