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.
Griffin AI runs on Anthropic's Claude models under the hood. Here's what the engine context, eval harness, and workflow scaffolding actually buy you over calling Claude directly.
Frontier models are remarkable reasoners, but security workflows demand more than raw intelligence. Here's how Griffin AI grounds frontier reasoning in real tenant context.
Reachability-grounded reasoning produces actionable findings. Ungrounded LLM reasoning produces speculation. We explain the methodology gap.
Frontier models are general polymaths. Security-specific LLMs are narrow experts. Choosing between them is rarely about raw intelligence and almost always about cost, latency, and the shape of your data.
Non-determinism is not a rough edge frontier labs will polish away. It is an architectural property of how transformer decoding works, and it places a hard ceiling on the kinds of security contracts you can sign.
Gemini Pro brings capable reasoning and a massive context window to general-purpose workflows. Griffin AI brings a security engine with an LLM on top. The difference matters when the workflow is appsec.
Prompt injection has evolved from demonstration exploits into a category of attack that runs continuously against production AI systems. Here is what changed in 2026.
A candid look at how Griffin AI's three-stage zero-day pipeline compares to pure-LLM Mythos-class bug hunters, and why false positive rates matter more than raw volume.
The confused deputy problem takes on new and subtle forms when AI agents invoke tools on behalf of users. A technical deep dive with concrete mitigations.
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