AI Security
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.
Mar 3, 20267 min read
Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
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 enforcement began in 2026. Vendors sold as "AI security tools" are now high-risk systems with documentation obligations. The shape of the documentation matters.
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 servers are privileged dependencies. An inventory that tracks them like SBOM tracks packages is the minimum bar — and not every tool meets it.
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 conditions are the hardest class of vulnerabilities for static analysis. Specific architectural capabilities separate tools that find them from tools that claim to.
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 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.
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.
Weekly insights on software supply chain security, delivered to your inbox.