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.
The datasets you use to evaluate model safety are themselves a supply chain, and almost nobody is treating them that way. A senior engineer's audit of how eval corpora get poisoned, contaminated, and silently drifted.
Most MCP threat models confuse protocol risk with deployment risk. Here is what the real attack surface looks like after a year of production incidents.
Griffin AI's auto-fixes compile clean 73 percent of the time and pass with minor edits 87 percent. Mythos-class pure-LLM patches rarely show those numbers for a reason.
Griffin uses Claude Opus as its deepest reasoning engine. Here's what triage looks like with Opus alone versus Opus running inside Griffin's eval harness.
SWE-bench became the default benchmark for measuring AI coding agents, but the security extensions that were bolted on afterwards deserve their own scrutiny. A field review of what they measure, where they break, and whether you should trust the numbers.
Codex-style coding agents are powerful for writing features. Security remediation needs a different shape of system—one that grounds frontier reasoning in SBOM, policy, and reachability context.
The EU Cyber Resilience Act wants mandatory vulnerability handling, SBOM delivery, and documented due diligence. Griffin AI produces those artifacts continuously. Mythos-class tools produce conversations about them.
Fine-tuning teaches a model to be a security expert. Grounding lets a general model act like one by reading the right sources. The right answer is usually both, but the proportions matter.
CycloneDX is not a text format to be summarized — it's a typed graph with dozens of semantically-rich fields. Griffin AI consumes it as a graph. Mythos-class tools consume it as tokens. That difference decides every downstream finding.
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