AI Security Scanning Orchestration Layer for Codebases

C7/10April 11, 2026

WhatAn intelligent harness that chunks entire codebases, routes code segments to optimal small models, cross-references findings across files, and filters false positives to surface real vulnerabilities.

SignalMultiple commenters converge on the same insight: the model is not the moat, the tooling is. The hard problem is not whether a model can spot a bug in an isolated function — it is how you decompose a million-line codebase, manage context windows, correlate findings across files, and suppress the noise. Nobody has built the definitive orchestration layer for this yet.

Why NowFrontier models like Mythos have proven AI can find real zero-days, legitimizing the category. Simultaneously, small open models have caught up on isolated analysis, meaning the orchestration layer can be model-agnostic and cost-effective.

MarketEnterprise security teams and open-source maintainers. $15B+ application security market. Existing SAST tools (Semgrep, CodeQL) are rule-based; AI-native competitors like Socket and Endor are early. The gap is a model-agnostic orchestration platform.

MoatProprietary pipeline logic for code decomposition, cross-file vulnerability chain detection, and a growing dataset of validated true/false positive classifications that improves filtering over time.

Small models also found the vulnerabilities that Mythos found View discussion ↗ · Article ↗ · 1,153 pts · April 11, 2026

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