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.
Unsafe deserialization looks obvious on a slide and impossible on a real codebase. Sinks are language-specific, gadgets live in third-party libraries, and the tainted byte can arrive wrapped in six layers of framework ceremony. Griffin's engine-plus-LLM design handles each of those concerns separately; Mythos-style pure-LLM scanners blur them into pattern-matching.
AI agents are consuming APIs, installing packages, and executing code autonomously. The security implications are massive and largely unaddressed.
In January 2024 a developer published npm packages that depended on every public npm package, triggering a denial-of-service style incident across the registry.
Pre-commit hooks feel like a free security win until you ship them at scale. Here are the failure modes, trust boundaries, and escape hatches that bite.
Shift-left security doesn't mean dumping security tools on developers. Here's a practical guide to integrating security into your development workflow without killing velocity.
Most AI bug hunters skip the hardest step: trying to kill their own findings. Here is why Griffin AI's disproof pass is the single biggest lever on false-positive rate.
Opus for reasoning, Sonnet for drafting, Haiku for scale. We break down when each tier earns its keep and why single-model architectures cannot compete.
A working engineer's comparison of Google Distroless, Chainguard Images, and Wolfi as base images, covering what actually breaks in production and what does not.
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