AI Security
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.
Feb 22, 20267 min read
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.
MCP servers are becoming a new dependency class with their own supply chain risks. How to think about registry governance, verification, and enterprise ingestion policy.
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.
Cursor Tab is excellent at in-editor autocomplete. For security review, the workflow is different enough that the right answer is to use both.
CSRF in 2026 is not the 2012 attack. SameSite cookies, fetch metadata, and modern frameworks changed the landscape. Detection needs to keep up.
Benchmark scores are only as honest as the dataset behind them. Griffin AI publishes golden-dataset design notes; Mythos-class tools rarely explain theirs.
AI-for-security procurement covers more than feature comparison. The due diligence checklist that surfaces structural differences between vendors.
CWE-502 deserialisation chains are the canonical stress test for AI bug hunters. Why Griffin AI's grounded synthesis finds real chains and Mythos-class scanners hallucinate them.
Weekly insights on software supply chain security, delivered to your inbox.