AI Security
Griffin AI vs Gemma for Lightweight Scanning
Gemma is built for efficiency. Can a small open-weight model replace Griffin AI for lightweight scanning workflows, or does the engine still matter?
Feb 11, 20267 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.
Gemma is built for efficiency. Can a small open-weight model replace Griffin AI for lightweight scanning workflows, or does the engine still matter?
The real cost of a scanner is not the subscription. It is the engineer hours lost to false positives, bad remediations, and noisy queues. We do the math.
What happens when the bug does not match any known CWE? A study of how grounded and pure-LLM scanners perform on genuinely novel vulnerability patterns.
When the test set is in the training set, the benchmark is broken. Security eval contamination is widespread and the mitigations are specific.
Anthropic's Claude Agent Skills let you package tools and context for Claude. Here's how that primitive compares to Griffin's security-specific workflow scaffolding.
A senior engineer's side-by-side look at Griffin AI and Mythos — why engine-grounded reasoning beats pure-LLM security intuition when the audit clock starts.
A million-token context window is a tool, not a solution. Context grounding for security requires architecture, not just capacity.
Reasoning models have arrived in security tooling. Evaluating them requires different methodology from evaluating classification or generation models. Here is what good evaluation looks like.
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.
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