The pressure

The Cause: A 'Vulnerability Tsunami' Triggered by AI

The story originates from a blog post titled "The pressure" by Daniel Stenberg, a core maintainer of curl. What is curl? It's one of the cornerstones of the internet, used by nearly every connected device for data transfer. For such a critical project, its security team has recently faced an unprecedented deluge of vulnerability reports: the rate is now 4-5 times higher than in 2024 and double that of 2025, averaging more than one report per day. Crucially, the quality of these reports is "way higher than ever before," typically being very detailed and lengthy. Stenberg admits this is an unprecedented level of pressure in his career, even causing his wife to express concern about his work-life balance.

Deconstruction: How AI Changed the Game in Security Research

The driving force behind this is AI, particularly Large Language Model (LLM)-assisted security research tools. Traditionally, security research required deep expertise and extensive manual analysis to find vulnerabilities. Now, AI can automate code auditing, pattern recognition, and potential vulnerability mining, dramatically lowering the barrier and time required for security research. This means a capable researcher, aided by AI tools, can achieve exponential growth in productivity. The high quality of reports stems from AI's ability to help generate extremely detailed technical analyses, clearly outlining the context, triggering conditions, and potential impacts of a vulnerability.

Trend Insight: AI is Reshaping the 'Labor-Intensive' Nature of Security

This reveals a deeper trend: AI is transforming security research from a 'craft' highly dependent on individual expert experience into a scalable 'industrial assembly line.' In the past, discovering a high-quality vulnerability might take a team months of dedicated work; now, AI-assisted tools might enable an individual to produce multiple reports of similar quality within days. This is a double-edged sword for the entire security ecosystem: on one hand, it can discover and patch software vulnerabilities at an unprecedented speed, enhancing overall security; on the other hand, it places an unbearable operational strain on foundational open-source projects like curl, which are often maintained by a small team of volunteers with limited resources. Facing the flood of reports generated by AI, they can easily become overwhelmed.

Practical Value: How Should We View and Respond to This?

For IT practitioners, especially developers and security engineers, this incident offers several insights: First, re-evaluate the risks and responsibilities associated with open-source dependencies. Every foundational library in your project could become a target for AI security research. You need to more proactively monitor the security advisories and maintenance status of these dependencies. Second, consider the application of AI tools in your own security processes. Whether for internal code audits or automated vulnerability verification, AI security tools are moving from cutting-edge to essential. Third, understand and support open-source maintainers. They are the unsung heroes of the digital world. The pressure exacerbated by AI requires the community to offer support in new ways (e.g., funding, automation tools, sharing review burdens).

Counter-intuitive/Unexpected: Amidst the Pressure, There's a Silver Lining

An easily overlooked positive point is that despite the surge in reports, the curl team has found that almost no 'terrible' high-severity vulnerabilities have been discovered in recent years; the vast majority are rated as low or medium severity. The most recent high-severity CVE dates back to October 2023. This indicates that curl's core code, having been refined over a long period, is already very robust. What AI is currently adept at finding are more edge cases and issues under specific configurations, rather than core architectural flaws. This perhaps suggests that AI's role in the security field is evolving from 'discovering fatal vulnerabilities' to 'large-scale cleanup of potential issues with known patterns,' which in itself represents an elevation of the overall security baseline.