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.
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.
AI models ship with dependencies, use vulnerable libraries, and introduce novel attack surfaces. Traditional scanning is not enough.
Claude Code MCP servers run with the privileges of the developer who invoked them. That makes deployment posture the entire security model.
Air-gapped AI is not a feature flag. It is an architectural commitment, and it separates serious enterprise products from consumer-grade assistants.
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.
Most enterprises rolled out AI-for-security tools faster than their governance processes could keep up. The resulting gap is where most of the pain from 2025 deployments lives.
Llama 3 is a powerful open-weight foundation model, but security workflows demand more than raw inference. Here is how Griffin AI compares.
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