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.
Injection vulnerabilities are not really about the sink. They are about the path from untrusted input to the sink. The path is where Griffin AI and Mythos-class tools diverge.
Open-weight models let you run everything locally. The tradeoff is quality, cost, and operational overhead. Griffin AI provides a different answer to the same on-prem need.
Fine-tuning inherits every problem of the base model and adds dataset provenance as a new one. Here is how detection actually works in practice.
GitHub Container Registry (ghcr.io) is convenient and tightly integrated with Actions, but the defaults can leak images and tokens. Here's how to lock it down properly.
Retrieval-augmented generation pipelines have become a primary breach vector for LLM products. The controls that contain the risk without breaking the use case.
Using an LLM to score another LLM's output is expedient and dangerous. The judge has its own biases — ones that affect security evaluations specifically.
Claude's Batch API gives you 50% off for async workloads. Griffin AI uses it internally. The question is whether your team should use the Batch API directly or consume it through Griffin.
Frontier models offer impressive enterprise features. Security programs need deeper controls than chat can provide—controls that live in the engine around the model.
The structural case for engine-plus-LLM security reasoning — and why pure-LLM products in the Mythos class hit a ceiling that no parameter count can raise.
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