AI Security
Eval Methodology: Griffin AI vs Mythos
A benchmark number is only as good as the methodology that produced it. Here is how Griffin AI builds its harness and why most Mythos-class tools cannot be audited.
Jan 13, 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.
A benchmark number is only as good as the methodology that produced it. Here is how Griffin AI builds its harness and why most Mythos-class tools cannot be audited.
SQL injection stopped being a single-line bug years ago. Modern chains stitch a tainted parameter through ORMs, caches, background jobs, and downstream services. Griffin AI's engine-plus-LLM architecture follows the taint across those hops; Mythos-class pure-LLM scanners summarise one file at a time and lose the thread.
Two AI bug hunters can both generate hypotheses. Only one can defend them. A field study of grounded versus ungrounded hypothesis generation in zero-day discovery.
Air-gapped AI is not a feature flag. It is an architectural commitment, and it separates serious enterprise products from consumer-grade assistants.
Tiered models and a deterministic engine cut token consumption to the moments that need reasoning. Pure-LLM tools pay full price for every trivial check.
Llama 3 is a powerful open-weight foundation model, but security workflows demand more than raw inference. Here is how Griffin AI compares.
Griffin AI produces draft PRs with taint paths, exploit hypotheses, and disproof attempts. Mythos-class pure-LLM tools skip those anchors, and PR quality suffers.
The NIST SSDF attestation form asks structured questions with structured answers. A chat transcript is not an answer. We explain how Griffin AI produces the evidence auditors expect, and why Mythos-class tools struggle.
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