AI Security
Sanitizer Detection: Griffin AI vs Mythos
A vulnerability that passes through a working sanitizer is not a vulnerability. Detecting that sanitizer accurately is the difference between actionable findings and noise.
Mar 18, 20265 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 vulnerability that passes through a working sanitizer is not a vulnerability. Detecting that sanitizer accurately is the difference between actionable findings and noise.
Your SBOMs come from a dozen vendors, three scanners, and two CI systems. Normalising them into one queryable graph is where SBOM programs actually succeed or fail.
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.
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.
Most security teams are sitting on hundreds of stale findings. Here is how to clear an aged vulnerability backlog with bulk remediation that actually merges.
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