| name | glasswing-vulnerability-discovery |
| description | AI-powered vulnerability discovery methodology from Anthropic's Project Glasswing - using frontier models (Mythos Preview) to find critical security vulnerabilities in open-source and enterprise software. |
| version | 1.0.0 |
| author | Anthropic Research |
| date | 2026-05-22T00:00:00.000Z |
| source | https://www.anthropic.com/research/glasswing-initial-update |
| category | cybersecurity |
| tags | ["cybersecurity","vulnerability-discovery","ai-security","frontier-models","mythos"] |
| activation_keywords | ["glasswing","vulnerability discovery","mythos preview","AI security testing","frontier red team"] |
Project Glasswing: AI Vulnerability Discovery
Methodology from Anthropic's Project Glasswing (May 2026) using Mythos Preview AI model to discover critical security vulnerabilities at unprecedented scale.
Core Concept
Project Glasswing uses frontier AI models (Mythos Preview) to scan software for vulnerabilities. Within one month, partners found 10,000+ high/critical vulnerabilities - a 10× increase in bug-finding rate.
Results Summary
| Metric | Value |
|---|
| Total vulnerabilities found | 10,000+ |
| Partner bug-finding rate increase | 10× |
| Open-source vulnerabilities scanned | 6,202 high/critical (estimated) |
| True positive rate (triaged) | 90.6% |
| High/critical confirmed | 62.4% |
Partner Examples
- Cloudflare: 2,000 bugs (400 high/critical), false positive rate better than human testers
- Palo Alto Networks: 5× increase in patches
- Microsoft: Continued trending larger patches
- Oracle: Multiple times faster vulnerability fixing
Vulnerability Disclosure Process
Follows industry convention:
- 90 days after discovery
- 45 days after patch becomes available
- Allows end users to update before exploitation risk
Key Vulnerability Types Found
- Critical Infrastructure: Internet infrastructure software
- Cryptographic Libraries: wolfSSL exploit construction
- Enterprise Systems: Cloud service vulnerabilities
- Open-Source Projects: 1,000+ projects scanned
Implementation Pattern
AI Vulnerability Scanning Workflow
1. Identify critical software targets
2. Run Mythos Preview scan
3. AI estimates severity (low/medium/high/critical)
4. Triage by independent security researchers
5. Validate true positives (90.6% rate achieved)
6. Confirm severity classification
7. Follow 90-day disclosure timeline
8. Patch deployment tracking
Exploit Construction Example
For wolfSSL vulnerability:
- Mythos Preview constructed working exploit
- Demonstrated actual exploitability
- Confirmed severity rating
