mythos
Safeguard articles tagged "mythos" — guides, analysis, and best practices for software supply chain and application security.
100 articles
Benchmark Reproducibility: Griffin AI vs Mythos
A benchmark you can't reproduce is marketing. A benchmark you can rerun on your own infrastructure is evidence. The reproducibility gap is wide.
Pricing Predictability: Griffin AI vs Mythos
A 40% cost surprise in year two is not a pricing issue — it is an architecture issue. Griffin AI and Mythos-class tools diverge on predictability in structural ways.
Cryptography Misuse Detection: Griffin AI vs Mythos
Crypto misuse is not about broken algorithms. It is about misused parameters, missing checks, and the gap between "it compiles" and "it is secure."
False Positive Rates: Griffin AI vs Mythos Benchmarked
Why pure-LLM security products generate false positives that engine-grounded platforms like Griffin AI structurally cannot — with CWEs and real triage data.
Support Model: Griffin AI vs Mythos
Support tier comparisons look identical on paper. The real difference shows up at 2am during an incident, and the shape of that difference is worth understanding before signing.
Rollback Safety: Griffin AI vs Mythos
Sometimes a remediation has to be reverted. Griffin AI's minimal, grounded patches roll back cleanly; Mythos-class patches often do not.
Transitive Depth: Griffin AI vs Mythos
Most scanners stop at five or six levels of transitive depth. Real production graphs run sixty levels deep, and the most interesting vulnerabilities live in the long tail.
CMMC Pass-Through: Griffin AI vs Mythos
CMMC 2.0 rollout has made flow-down expectations concrete. AI-for-security tools used by DIB contractors are in scope, and the pass-through story matters.
Training Data Provenance: Griffin AI vs Mythos
Training data is a supply chain component. Knowing what went into a model is the precondition for knowing what could come out of it. Few tools track this; the few that do matter disproportionately.
Cost Per Finding: Griffin AI vs Mythos
Token spend per scan is the wrong metric. Cost per actionable finding is the right one — and it's where engine-plus-LLM economics dominate pure-LLM economics.
Dependency Confusion: Griffin AI vs Mythos
Dependency confusion is older than most of the AI tooling trying to detect it. The attacks have adapted to the defences — detection needs to keep up.
Elastic Scale Behaviour: Griffin AI vs Mythos
Scanning bursts when a monorepo merges. We explain why Griffin AI absorbs the spike gracefully while Mythos-class tools degrade into rate-limit queues.
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