AI Security
Griffin AI vs Open Weights: The Eval Gap
Frontier models pass eval benchmarks that open-weight models miss by specific measurable margins. For security workflows, the gap matters.
Mar 7, 20263 min read
Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
Frontier models pass eval benchmarks that open-weight models miss by specific measurable margins. For security workflows, the gap matters.
ML research has a reproducibility crisis. AI security evaluation inherits it. Vendors publishing numbers that can't be reproduced are the norm — not the exception.
Claude's prompt caching gives you 90% discount on cached tokens. Security workloads have massive cacheable surface area. Griffin AI takes advantage; direct API use often does not.
Auth bypasses are rarely a single bug. They live in the interaction between layers — middleware, route handlers, framework annotations. Finding them requires path analysis across abstraction layers.
Chain-of-thought helps LLMs with multi-step problems. For vulnerability reasoning, it helps — but only when the chain is grounded in structured evidence.
Context-window size matters less than context quality. A look at how Griffin AI's engine-grounded context beats pure-LLM retrieval at monorepo scale.
The OpenAI Assistants API is a general agent framework. SecOps needs more than a framework — it needs the engine-grounded reasoning Griffin AI adds on top.
The model you think you're calling might not be the model that returns. Model substitution is a quiet supply chain risk that deserves explicit controls.
Gemini's pricing table favours long-context workloads. Security scans have long-context structure. The question is how much context fits into the architecture.
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