Small models also found the vulnerabilities that Mythos found (aisle.com)

Article summary

The article discusses the capabilities of small, cheap models in detecting vulnerabilities, comparing them to Anthropic's Mythos model. The authors found that small models can recover much of the same analysis as Mythos when given the relevant code and contextual hints. The article suggests that the key to successful vulnerability detection lies in the system and scaffolding, rather than the model itself. The authors argue that their approach can provide a more cost-effective and efficient way to detect vulnerabilities.

Main themes

• AI cybersecurity
• Vulnerability detection
• Model capabilities
• Scaffolding and system design
• Cost-effectiveness
• Autonomous discovery

What commenters say

• The article's results are impressive, but the approach of isolating relevant code and providing hints may not be representative of real-world scenarios.
• Small models can be effective in detecting vulnerabilities if provided with the right context and scaffolding, but may not be able to handle complex, multi-file vulnerabilities.
• The value of a model lies not in its size or capabilities, but in the system and workflow it is a part of, including targeting, iterative deepening, validation, and triage.
• The article's findings do not necessarily dispel the impressive results of Anthropic's Mythos model, as the two approaches may be addressing different aspects of vulnerability detection.
• The use of small models and scaffolding can be a cost-effective way to detect vulnerabilities, but may require significant computational resources to scan entire codebases.
• The effectiveness of a model in detecting vulnerabilities is not solely determined by its size or capabilities, but also by its ability to handle complex contexts and avoid false positives.
• The article's results highlight the importance of considering the broader system and workflow when evaluating the capabilities of AI models in cybersecurity.
• The comparison between small models and Anthropic's Mythos model may not be entirely fair, as the latter may have been designed to handle more complex and nuanced vulnerability detection tasks.