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.
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.
A detailed comparison of how Griffin AI consumes SBOMs as structured reasoning context while Mythos-class pure-LLM tools skim them as prose — and why that architectural gap determines the quality of every downstream finding.
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.
Griffin AI publishes a five-family eval harness with concrete numbers. Most Mythos-class competitors ask buyers to trust marketing claims instead of data.
When AI writes code that ships to production, the audit trail is a compliance requirement, not a nice-to-have. Patterns for capturing it without killing velocity.
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.
Why enterprise AI for security requires genuine on-premises deployment, not just a SaaS endpoint with a VPN in front of it.
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.
Indirect prompt injection arrives through your retrieval corpus, not your chat box. We cover the detection strategies that survive when attackers write your RAG content.
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