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.
SQL injection stopped being a single-line bug years ago. Modern chains stitch a tainted parameter through ORMs, caches, background jobs, and downstream services. Griffin AI's engine-plus-LLM architecture follows the taint across those hops; Mythos-class pure-LLM scanners summarise one file at a time and lose the thread.
A benchmark number is only as good as the methodology that produced it. Here is how Griffin AI builds its harness and why most Mythos-class tools cannot be audited.
Two AI bug hunters can both generate hypotheses. Only one can defend them. A field study of grounded versus ungrounded hypothesis generation in zero-day discovery.
Claude Code MCP servers run with the privileges of the developer who invoked them. That makes deployment posture the entire security model.
AI models ship with dependencies, use vulnerable libraries, and introduce novel attack surfaces. Traditional scanning is not enough.
Tiered models and a deterministic engine cut token consumption to the moments that need reasoning. Pure-LLM tools pay full price for every trivial check.
Air-gapped AI is not a feature flag. It is an architectural commitment, and it separates serious enterprise products from consumer-grade assistants.
Griffin AI produces draft PRs with taint paths, exploit hypotheses, and disproof attempts. Mythos-class pure-LLM tools skip those anchors, and PR quality suffers.
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