Benchmark Reproducibility: Griffin AI vs Mythos
A benchmark you can't reproduce is marketing. A benchmark you can rerun on your own infrastructure is evidence. The reproducibility gap is wide.
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 benchmark you can't reproduce is marketing. A benchmark you can rerun on your own infrastructure is evidence. The reproducibility gap is wide.
Prompt injection is the defining AI security problem of this generation. The defences are structural, not cosmetic — and the architectural choices show.
Windsurf's Cascade agent is among the more capable in-editor agents. For security review specifically, it's a complement to Griffin AI, not a replacement.
A taint path is not an exploit. Here is how a zero-day pipeline turns a reachable flow into a defensible proof-of-concept payload without inventing a vulnerability.
Ethical hacking means testing systems with permission to find flaws before criminals do. Here is what it actually involves, the phases, the skills, and where AI fits in.
AI-for-security metrics that show up on board slides are different from the ones engineers use day-to-day. Designing both sets properly is the work.
Self-hosting Llama looks cheap on paper. The real costs — GPUs, operations, engineering — make the comparison less obvious than the list price suggests.
A 40% cost surprise in year two is not a pricing issue — it is an architecture issue. Griffin AI and Mythos-class tools diverge on predictability in structural ways.
The MCP client surface is often overlooked. We examine trust boundaries, schema handling, credential storage, and safe defaults for the agent side of the protocol.
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