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ai-safety developer-tools frontier-ai security-models

Build your own vulnerability harness

blog.cloudflare.com

July 10, 2026

25 min read

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43/100

Summary

Project Glasswing examines the impact of frontier security models on enterprise codebases and the adaptability of defensive structures against threats from frontier AI. The evolving AI ecosystem poses risks to developers reliant on single models, highlighting the need for robust vulnerability harnesses.

Key Takeaways

Project Glasswing explores the use of frontier security models on enterprise codebases to enhance vulnerability detection and protection against AI threats.
A model-agnostic architecture is essential for effective vulnerability scanning, allowing for interchangeable models that provide diverse perspectives on code analysis.
The proposed system emphasizes the importance of cross-referencing findings from different models to improve the accuracy of vulnerability detection and triage.
A successful vulnerability harness must support persistent investigations and fleet-wide dependency tracing, rather than relying on isolated subagents or single models.

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Community Sentiment

Mixed

Positives

The idea of increasing the cost of insecure coding could be a game changer, forcing businesses to prioritize security over profit margins.
Creating a custom harness lets you optimize for token costs, which is crucial when scaling vulnerability scanning — efficiency is king!
Open sourcing effective tools like VISA's harness is a big win for the community, enabling better security practices with accessible resources.

Concerns

The current token burning process feels like a shakedown, turning what should be a value-add into a costly nightmare for developers.
There's a real concern that AI models aren't trained to recognize security implications, leaving developers to fend for themselves.
If AI turns coding into a costly endeavor rather than a cheap one, it undermines the entire value proposition of using AI in software development.