mythos
Safeguard articles tagged "mythos" — guides, analysis, and best practices for software supply chain and application security.
100 articles
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.
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.
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.
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.
False Positive Cost: Griffin AI vs Mythos
A false positive is not free. It costs engineer attention, trust in the tool, and eventually the security programme's credibility. We price the difference.
Injection Path Detection: Griffin AI vs Mythos
Injection vulnerabilities are not really about the sink. They are about the path from untrusted input to the sink. The path is where Griffin AI and Mythos-class tools diverge.
Why Engine-Plus-LLM Beats Pure-LLM: Griffin vs Mythos
The structural case for engine-plus-LLM security reasoning — and why pure-LLM products in the Mythos class hit a ceiling that no parameter count can raise.
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