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 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.
GitHub Copilot suggests fixes. Griffin AI generates fix PRs with taint paths and disproof attached. The difference is review burden.
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.
Open-weight models let you run everything locally. The tradeoff is quality, cost, and operational overhead. Griffin AI provides a different answer to the same on-prem need.
Claude's Batch API gives you 50% off for async workloads. Griffin AI uses it internally. The question is whether your team should use the Batch API directly or consume it through Griffin.
Weekly insights on software supply chain security, delivered to your inbox.