AI Security
Scaling Across Repos: Griffin AI vs Mythos
Multi-repo security reasoning is a graph problem, not a retrieval problem. How Griffin AI's engine scales where pure-LLM products flatten into guesswork.
Mar 28, 20266 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.
Multi-repo security reasoning is a graph problem, not a retrieval problem. How Griffin AI's engine scales where pure-LLM products flatten into guesswork.
Fine-tuning to improve one task frequently regresses others. Without eval harnesses, the regressions ship. The measurable drift is larger than vendors admit.
The difference between grounded reasoning and hallucinated reasoning is not eloquence — it's citation. A look at how Griffin AI anchors every claim.
Pure-LLM security analysis hallucinates findings at rates between 20% and 70% depending on the task and model. Grounding is the architectural answer.
Why pure-LLM security products generate false positives that engine-grounded platforms like Griffin AI structurally cannot — with CWEs and real triage data.
Context-window size matters less than context quality. A look at how Griffin AI's engine-grounded context beats pure-LLM retrieval at monorepo scale.
The model you think you're calling might not be the model that returns. Model substitution is a quiet supply chain risk that deserves explicit controls.
The structural case for engine-plus-LLM security reasoning — and why pure-LLM products in the Mythos class hit a ceiling that no parameter count can raise.
Retrieval context poisoning scales differently than direct prompt injection. The attacker's leverage grows with the RAG ingest surface.
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