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.
Stored, reflected, DOM, mutation, and template-injection XSS each live in a different part of the application and demand a different analysis. Griffin's engine understands template contexts, framework escaping rules, and client-side sinks; Mythos reads HTML and hopes. The difference shows up the moment you leave textbook territory.
Every release risks making the model worse. Griffin AI's regression gates block bad builds before they ship. Mythos-class tools rarely describe a gate process at all.
Cursor's 2026 security model introduces privacy modes, indexing controls, and agent sandboxes. Here is the enterprise-ready view of what works.
A field guide to container-specific vulnerability management tools: what Trivy, Grype, Clair, Docker Scout, and Anchore actually catch, and where each falls short.
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.
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.
Gemma is built for efficiency. Can a small open-weight model replace Griffin AI for lightweight scanning workflows, or does the engine still matter?
AI incidents are not the same shape as traditional security incidents. The playbooks need to be specific to how AI systems actually fail.
The most grounded concerns about AI are not sci-fi scenarios; they are prompt injection, data leakage, supply chain risk in models, and opaque decisions. Here is how each one actually shows up.
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