Mythos
Safeguard articles tagged "Mythos" — guides, analysis, and best practices for software supply chain and application security.
103 articles
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.
Auth Bypass Discovery: Griffin AI vs Mythos
Auth bypasses are rarely a single bug. They live in the interaction between layers — middleware, route handlers, framework annotations. Finding them requires path analysis across abstraction layers.
Context Window Limits: Griffin AI vs Mythos
Context-window size matters less than context quality. A look at how Griffin AI's engine-grounded context beats pure-LLM retrieval at monorepo scale.
Onboarding Velocity: Griffin AI vs Mythos
Time from contract signature to first meaningful finding is the metric procurement cares about. Griffin AI and Mythos-class tools diverge in week one.
Transitive Fix Cascades: Griffin AI vs Mythos
A vulnerable transitive dependency may require upgrading an ancestor. Griffin AI computes the cascade; Mythos-class tools often stop at the first level.
EU AI Act Alignment: Griffin AI vs Mythos
EU AI Act enforcement began in 2026. Vendors sold as "AI security tools" are now high-risk systems with documentation obligations. The shape of the documentation matters.
Version-Aware Resolution: Griffin AI vs Mythos
A vulnerability in version 1.2.0 may not affect your 1.3.5 install if the fix reshaped the call signature. Version-aware resolution is where deterministic engines beat pure-LLM heuristics.
MCP Server Inventory: Griffin AI vs Mythos
MCP servers are privileged dependencies. An inventory that tracks them like SBOM tracks packages is the minimum bar — and not every tool meets it.
Continuous Eval & Release Gating: Griffin AI vs Mythos
Evals that run once are marketing. Evals that run on every build are infrastructure. Griffin AI runs the harness on every change; Mythos does not describe one.
Race Condition Detection: Griffin AI vs Mythos
Race conditions are the hardest class of vulnerabilities for static analysis. Specific architectural capabilities separate tools that find them from tools that claim to.