Why does my security scanner miss the vulnerability class my AI assistant writes most?

AI coding assistants introduce a security vulnerability in 44 to 72 percent of generation tasks depending on language, according to Veracode's 2026 GenAI Code Security Report. Existing SAST tools were designed and tuned against human-written code patterns; they do not model the repeating, model-specific vulnerability signatures that a given AI tool produces. A July 2026 cross-model comparative study on arXiv found that different LLMs concentrate risk in predictable, distinct CWE classes across entirely unrelated codebases, and that current scanners have no model-aware profiles to exploit this regularity. Endorlabs confirmed in 2026 that fixed-pattern scanners miss flaws that depend on cross-file context, which is exactly where AI-generated code most often fails. No production-grade scanner today ships profiles keyed to the model that generated the code.