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.
A quarterly recap of everything Safeguard.sh shipped in Q3 2025 across Griffin, Eagle, Lino, and Gold — with the improvements, deprecations, and next steps.
A retrospective on Safeguard v5's first year in production, the features that resonated, and where we're headed next.
CVE-2024-53677 lets attackers abuse Struts file upload parameter pollution to plant webshells. Here is the chain, detection logic, and patch guidance.
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