How to Assess AI-Powered Code Analyzers for Vulnerability Hunting (Inspired by the Curl Case)

Introduction

When Anthropic unveiled its Mythos model, much hype surrounded its ability to spot security flaws in source code. But Daniel Stenberg, the creator of curl, decided to put it to the test. After running Mythos on the curl repository—a project he knows intimately—he found that while Mythos did find vulnerabilities, it didn't outperform existing AI tools by a significant margin. His conclusion: AI code analyzers are generally excellent at finding bugs, but no single model is a silver bullet. This guide will walk you through your own assessment of AI-powered code analyzers, using Stenberg's approach as a blueprint. By the end, you'll know how to set up, run, and critically evaluate tools like Mythos (or any other AI analyzer) on your own codebase, so you can separate genuine improvement from marketing hype.

What You Need

• Target source code repository – A project you are familiar with (like curl or your own code).
• An AI-powered code analyzer – Options include GPT-4, Claude (Anthropic's model), Copilot, or specialized tools like Snyk Code. For this guide, we use 'Mythos' as an example.
• Traditional static analysis tools (optional) – e.g., Clang Static Analyzer, Coverity, or Flawfinder for baseline comparison.
• A computer with internet access and appropriate API keys for the AI tool.
• Basic familiarity with the command line and your codebase's build system.
• Patience and experimental spirit – as Stenberg notes, anyone can find security issues with enough time and curiosity.

Step-by-Step Process

Step 1: Choose a Representative Codebase

Select a small to medium-sized open-source project that you understand well. Stenberg used curl because he authored it. Familiarity helps you verify false positives and judge the significance of findings. Ensure the repository is stable, has active bug tracking, and includes known vulnerabilities for benchmark testing.

Step 2: Prepare the Code for Analysis

Check out a clean copy of the repository. If the project uses C/C++ (like curl), run a build to confirm it compiles. Some AI tools need the source tree intact, while others accept patches or specific files. For Mythos, Stenberg presumably provided the full repository. Document the version you test (e.g., commit hash) so results are reproducible.

Step 3: Run a Baseline Scan with Traditional Tools

Before invoking AI, run a classic static analyzer to establish a baseline. Use tools like Clang Static Analyzer or Flawfinder. Record the number and types of issues found. This gives you a benchmark to compare against the AI tool’s performance. Stenberg likely had years of experience with traditional scanning, so he knew what to expect.

Step 4: Execute the AI Analyzer (Mythos or Equivalent)

Access the AI tool via API or web interface. Provide the entire codebase or specific files, depending on the tool’s capabilities. For Mythos, you would submit the repository and ask it to find security vulnerabilities. Be specific – prompt like “Analyze this code for memory corruption, buffer overflows, and injection flaws.” Wait for the output, which may include a list of potential issues, with descriptions and code locations.

Step 5: Compare and Contrast Findings

Now the manual work begins. Cross-reference the AI’s output with your baseline. Stenberg emphasized that Mythos found issues, but often the same ones that other AI tools (or even simpler scanners) could detect. Classify each finding:

• True positive – actual vulnerability missed by baseline.
• False positive – not a real bug.
• Duplicate – already reported by traditional tools.
• Low severity – minor or improbable exploit.

Stenberg noted that Mythos did not uncover any “magical” new class of bugs; its advantages were incremental at best.

Step 6: Assess the Practical Impact

Ask yourself: Would any of these findings require a security advisory? Could they be exploited in a realistic attack? Stenberg concluded that Mythos did not produce a “significant dent” in the code’s security posture—most issues were mundane. If the AI finds many critical flaws your manual review missed, that’s a win. But if it mainly echoes known patterns, it’s less valuable.

Step 7: Repeat with Different AI Models

To get a broader picture, run the same codebase through multiple AI analyzers (e.g., GPT-4, CodeQL, or other cloud services). Compare their hit rates, false positive rates, and the nature of suggestions. This mirrors Stenberg’s experience: he could only comment on what Mythos found for curl, but he suspected other models would perform similarly.

Step 8: Draw Your Conclusion

Document your findings in a report. Include metrics like total issues found, unique vulnerabilities, and time taken. Stenberg’s key point was that AI code analyzers are substantially better than any pre-AI tool, but the hype around a particular model may be overblown. If your analysis shows one tool is only marginally better than others, weigh the cost (API fees, learning curve) against the benefit.

Tips for a Fair Evaluation

• Use the same input for all tests – keep the codebase version fixed.
• Watch for confirmation bias – don’t overvalue results that match your expectations.
• Consider the tool’s training data – if your code is similar to public repos, the AI might regurgitate known patterns.
• Remember human oversight is still crucial – Stenberg underscores that “high quality chaos” means many issues need triage.
• Don’t skip traditional tools – they are fast, free, and cover fundamentals well.
• Check the model’s novelty – ask if it finds issues that no other tool flagged; that’s the real test of advancement.

By following these steps, you can replicate Stenberg’s assessment and make an informed decision about adopting AI code analyzers. The curl case shows that while AI is powerful, you should temper expectations with rigorous testing.