Source/Sink Classification: Griffin AI vs Mythos
Taint analysis only works if sources and sinks are labeled correctly. Griffin AI uses a curated catalog; Mythos-class tools infer on the fly.
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.
Taint analysis only works if sources and sinks are labeled correctly. Griffin AI uses a curated catalog; Mythos-class tools infer on the fly.
HIPAA's software supply chain expectations have sharpened in 2025-2026. Evidence generation is the difference between passing an audit and rerunning it.
Multi-agent systems inherit every trust problem of single-agent systems and add a few more. Here is how the threat model actually shifts.
You cannot secure what you cannot enumerate. Griffin AI maintains a typed inventory of every model, version, and deployment across a tenant. Mythos-class tools approximate the inventory in prose.
Poisoned AI models are a supply chain threat that traditional security tools can't detect. Here are the emerging techniques for identifying compromised models.
Benchmark scores are only as honest as the dataset behind them. Griffin AI publishes golden-dataset design notes; Mythos-class tools rarely explain theirs.
CSRF in 2026 is not the 2012 attack. SameSite cookies, fetch metadata, and modern frameworks changed the landscape. Detection needs to keep up.
Cursor Tab is excellent at in-editor autocomplete. For security review, the workflow is different enough that the right answer is to use both.
Securing container software means controlling the whole chain, base image, dependencies, build, registry, and runtime, not just scanning the final image. Here is a working model for each layer.
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