AI Security
Cache Hit Optimisation: Griffin AI vs Mythos
Prompt caching and engine memoisation combine to make Griffin AI scans repeat-cheap. Pure-LLM tools recompute the same reasoning on every run.
Jan 27, 20266 min read
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Prompt caching and engine memoisation combine to make Griffin AI scans repeat-cheap. Pure-LLM tools recompute the same reasoning on every run.
Getting the CWE right is not a taxonomic hobby. It drives remediation, compliance mapping, and detection engineering. Here is how grounded and pure-LLM scanners compare.
Data residency is no longer a procurement checkbox. It is an architectural property that most pure-LLM vendors cannot deliver without major rework.
A remediation PR is only useful if it does not break anything else. Griffin AI runs targeted regression before opening; Mythos-class tools usually do not.
SPDX is the format auditors ask for, the format regulators reference, and the format most enterprise procurement teams standardize on. Griffin AI treats it as a first-class graph. Mythos-class tools treat it as a long document.
FedRAMP HIGH demands 421 controls with documented, continuous evidence. Griffin AI produces control-mapped records every day. Mythos-class pure-LLM tools cannot fill a 3PAO evidence package.
Taint tells you whether attacker data actually reaches a sink. Griffin AI propagates it; Mythos-class tools infer it. The difference shows up fast.
Griffin AI reports 98-100% hold rate against adversarial probes. Most Mythos-class tools have never published an adversarial number at all.
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.
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