AI Security
CSRF Modern Forms: Griffin AI vs Mythos
CSRF in 2026 is not the 2012 attack. SameSite cookies, fetch metadata, and modern frameworks changed the landscape. Detection needs to keep up.
Feb 20, 20263 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.
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.
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.
A shrinking triage queue is the clearest sign a security programme is working. We explain why Griffin AI shrinks queues and Mythos-class tools grow them.
Fine-tuning an open-weight model sounds like a shortcut to a custom SecOps copilot. In practice, it is one step of a much longer journey.
Claude Desktop's MCP support makes it a capable security tool. Griffin AI builds on that foundation rather than competing with it.
An architectural comparison of Griffin AI's engine-grounded reasoning stack against the pure-LLM pattern that Mythos-class products rely on.
Function calling gives models the ability to act. Acting safely on behalf of a specific user, in a specific context, within specific policy is a different problem.
Model lock-in is the quiet liability of pure-LLM vendors. Safeguard's bring-your-own-model story gives enterprises the option Mythos-class competitors cannot match.
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