For several months, the technology world has been captivated by the promise of Artificial Intelligence in the field of cybersecurity. One of the most significant headlines involved Anthropic’s “Mythos” model, a tool designed to scan complex codebases and identify hidden vulnerabilities. However, recent feedback from the community suggests that this high-profile exercise may have been more about public relations than actual security.
Daniel Stenberg, the creator of cURL—a fundamental tool used by millions of systems globally—recently shared his experience with the Mythos model. His conclusion was blunt: the exercise was an “amazingly successful marketing stunt.”
The Reality Behind the Results
When the Mythos model was tasked with scanning the cURL codebase, the expectations were high. However, the results were underwhelming. The tool initially flagged five “confirmed security vulnerabilities.” Upon closer inspection by the expert cURL security team, the reality was quite different.
Three of the findings were identified as false positives, pointing to issues already addressed in the official documentation. One was a minor software bug with no security implications. Only one single vulnerability was confirmed, and it was classified as “low severity.” This discrepancy highlights a common issue with automated AI tools: they often generate noise that requires significant human expertise to filter.
A Lack of Balance in Reporting
A significant criticism of this project is how the results were presented to the public. The Mythos model reportedly scanned a vast number of open-source packages on GitHub. Despite this wide-reaching analysis, the reports focused exclusively on the vulnerabilities it claimed to find.
There was almost no information provided regarding the projects where the model found no vulnerabilities. In a genuine scientific or educational effort, knowing which systems are secure is just as important as knowing which are not. By only highlighting the “flaws,” the project missed a vital opportunity to provide a balanced view of the current state of open-source security.
The Missed Opportunity for Learning
The true value of such an extensive scan would have been in the data regarding successful protection. If the model found a particular project to be highly resilient, the industry would benefit immensely from knowing:
- Which coding styles were most effective at resisting automated vulnerability detection?
- Which programming languages appeared to be more inherently secure in this context?
- How did the age and maturity of the code correlate with its security posture?
By withholding this information, the project served only to promote the capabilities of the AI rather than to educate the developer community. We are left with a “hype cycle” that prioritises alarming headlines over the constructive learning that comes from studying good examples of secure code.
Moving Beyond the AI Hype
At Vertex, we understand that while AI is a powerful tool, it is not a substitute for a comprehensive and human-led security strategy. Relying solely on automated scans can lead to a false sense of security or, conversely, a waste of resources chasing false positives.
A robust defence involves a layered approach. This includes high-quality coding practices, regular manual reviews, and the use of tools that provide actionable insights rather than just marketing data. It is important to remember that security is a continuous process of improvement, not a one-time scan from a “magic” tool.
If your organisation is looking for a deeper understanding of its security posture, consider moving beyond the hype. Professional assessments and tailored strategies can help identify real risks and reinforce your defences effectively.
We encourage you to contact the team at Vertex for a professional consultation on how to secure your systems. Our experts can provide the clarity and expertise needed to ensure your organisation is truly protected. Alternatively, you may visit the Vertex website for further information on our range of services.