Mythos
Safeguard articles tagged "Mythos" — guides, analysis, and best practices for software supply chain and application security.
103 articles
Sanitizer Detection: Griffin AI vs Mythos
A vulnerability that passes through a working sanitizer is not a vulnerability. Detecting that sanitizer accurately is the difference between actionable findings and noise.
Cross-Vendor SBOM Normalization: Griffin AI vs Mythos
Your SBOMs come from a dozen vendors, three scanners, and two CI systems. Normalising them into one queryable graph is where SBOM programs actually succeed or fail.
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.
Prompt Injection Defences: Griffin AI vs Mythos
Prompt injection is the defining AI security problem of this generation. The defences are structural, not cosmetic — and the architectural choices show.
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.
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.
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.
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.