Mythos: Given Enough Inference, All Bugs Are Shallow (corgea.com)

Anthropic's latest release, Mythos, introduces a new paradigm for vulnerability scanning in software security, encapsulating the idea that "given enough inference, all bugs are shallow." Unlike the traditional approach of relying on collective human scrutiny (Linus's Law), Mythos enables a single attacker to identify high-severity vulnerabilities in mere minutes, highlighting a paradigm shift in security dynamics. A recent benchmark comparing popular models—including Claude Opus 4.6 and Corgea versions using GPT-4.1 and GPT-5.4—revealed that while newer models like GPT-5.4 demonstrated enhanced precision and fewer false positives, raw model capability alone isn’t a silver bullet. Corgea's purpose-built architecture significantly outperformed Claude, reflecting that effective vulnerability scanning depends not just on model sophistication but also on the contextual framework in which it operates. This shift poses significant implications for the AI/ML community, particularly in application security (AppSec). As vulnerability detection increasingly leans on advanced inference models, the challenge lies in balancing model performance against operational costs. Traditional security tools often miss a substantial portion of vulnerabilities, but LLMs can bridge that gap. The report emphasizes the growing economic challenge of high inference costs and the need for a nuanced understanding of how architecture and model capabilities interact to yield actionable security outcomes. Companies must navigate this evolving landscape carefully, ensuring they invest in effective security solutions without falling into the trap of merely acquiring pardonable inference-heavy tools.