AI-Assisted Static Analysis Uncovers Potential Issues in Curl: Insights from Hacker News
A recent Hacker News discussion reveals how AI tools successfully identified legitimate security issues in the curl library, highlighting a promising application of large language models for code review rather than code generation.
The Breakthrough
Security researcher Joshua Rogers used specialized AI-assisted static analysis tools to scan curl’s codebase and discovered over 40 potential issues. Curl maintainer Daniel Stenberg confirmed that 22 of these reports led to actual bug fixes—a stark contrast to the flood of AI-generated false reports that typically plague open source projects.
This success story stands out because Stenberg has been vocal about the problems with AI-generated security reports, previously describing them as “slop” that wastes maintainer time. The difference here: Rogers used professional-grade tools and validated findings before reporting them.
The Right Tool for the Right Job
The Hacker News community identified a key insight: AI excels at analysis and review, not generation. As one developer noted, “Don’t write or fix the code for me, but instead tell me which places in the code look suspicious and where I need to have a closer look.”
This approach leverages AI’s pattern recognition strengths while avoiding its weaknesses in code generation. Traditional static analysis tools often produce too many false positives to be useful. AI-assisted tools can filter these results and provide context that makes the output actionable.
Tools That Made the Difference
Rogers used several specialized security analysis tools:
- ZeroPath: An AI-native static analyzer that applies security rules across codebases and uses LLMs to determine if issues are genuine
- Corgea: A tool that combines traditional static analysis with AI-powered triage
- Almanax: Another AI-assisted security scanner
These aren’t simple ChatGPT queries—they’re purpose-built tools that combine traditional program analysis with LLM reasoning to reduce false positives and improve accuracy.
The Automation Debate
The discussion revealed an interesting tension about what tasks should be automated. Many developers expressed frustration that AI automates creative work (coding) while leaving mundane tasks (laundry, cleaning) to humans.
One developer captured this sentiment: “I want an AI that can do my laundry, fold it, and put it away. I don’t need an AI to write code for me.”
However, others found AI enhanced their creativity by handling routine implementation details, allowing them to focus on architecture and design decisions.
Professional vs. Amateur Usage
The curl success story highlights the importance of expertise in AI tool usage. Professional security researchers who understand vulnerabilities can effectively validate AI findings. Amateur users who blindly submit AI-generated reports create noise that wastes maintainer time.
This pattern extends beyond security research. Developers with clear requirements and tight boundaries report better results from AI coding assistants, while those using AI as an “autopilot” often struggle with quality and maintainability.
Implementation Insights
The most effective approach appears to be using AI as an intelligent filter rather than a primary analyzer. Traditional static analysis tools generate comprehensive reports that humans find overwhelming. AI can process these reports, understand context across files, and highlight the most significant issues.
This “verbose analyzer → LLM triage” architecture leverages the strengths of both approaches: comprehensive detection from traditional tools and intelligent prioritization from AI.
Looking Forward
The curl findings suggest AI-assisted code analysis has genuine potential when used professionally. The key factors for success include:
- Using specialized tools designed for security analysis
- Having domain expertise to validate findings
- Treating AI as a force multiplier rather than a replacement
- Focusing on analysis and review rather than generation
As one security professional noted, this represents a shift toward developers becoming “directors” and “reviewers” where communication skills—the ability to clearly describe problems to both humans and AI—become increasingly valuable.
The curl case study demonstrates that AI can be a powerful ally in software security when wielded by experts who understand both the technology’s capabilities and limitations.