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.
When an agent can call tools, the permission boundary is no longer between the user and the system. It is between the model's current beliefs and everything the model can reach. That is a much harder boundary to defend.
Model weights are binaries with the privilege of code and the review of documents. Here is what signing, attestation, and provenance should actually look like.
Large language models have their own supply chains: training data, fine-tuning datasets, model weights, and serving infrastructure. Each layer introduces risk.
A remediation PR explanation is either evidence or storytelling. Griffin AI attaches taint paths and disproof attempts; Mythos-class tools attach plausible prose.
The AI Bill of Materials went from concept paper to procurement requirement in under two years. Here is what the current state of the art actually looks like.
Dynamic dispatch hides real exploits behind indirection. Griffin AI models the dispatch; Mythos-class tools guess. That gap changes outcomes.
An AI-based security system uses machine learning to detect threats and prioritize risk at a scale humans cannot match. Here is what it does well, and where it needs guardrails.
Output filters are the last line before the user and the tool call. We cover when they work, when they fail, and how to measure them honestly in production.
VEX is how you turn a vulnerability list into an actionable work queue. Griffin AI ingests VEX documents as structured statements that filter findings at policy time. Mythos-class tools read them as advisory prose and lose the filtering entirely.
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