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.
A working breakdown of the OWASP Top 10 for Large Language Model Applications — what each risk actually looks like in production and how teams are testing for it.
SLSA provenance is the cryptographic receipt of a build. Griffin AI verifies it, parses it, and uses it as typed evidence. Mythos-class tools describe it and forget to check the signature.
Copilot's code review is useful. It is also not a security review, and treating it as one is how vulnerabilities ship. Here is what it actually catches.
Cursor's 2026 security model introduces privacy modes, indexing controls, and agent sandboxes. Here is the enterprise-ready view of what works.
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.
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.
Reka's multimodal models are interesting for specific security workflows. The question is whether multimodal is the binding constraint, and usually it isn't.
A field guide to container-specific vulnerability management tools: what Trivy, Grype, Clair, Docker Scout, and Anchore actually catch, and where each falls short.
AI incidents are not the same shape as traditional security incidents. The playbooks need to be specific to how AI systems actually fail.
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