AI Security
Ensemble LLMs For High-Precision Security Findings
One model's confident answer is a guess. Multiple models agreeing is evidence. Ensemble approaches raise precision for security-critical findings.
Mar 13, 20262 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.
One model's confident answer is a guess. Multiple models agreeing is evidence. Ensemble approaches raise precision for security-critical findings.
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.
Fine-tuning a model on an attacker-controlled dataset can implant behaviour that only activates under specific conditions. The threat is quiet because detection is hard.
AI red teaming is not a one-off exercise. Programmatic red-teaming of AI systems requires specific structure — and most organisations don't have it yet.
ML research has a reproducibility crisis. AI security evaluation inherits it. Vendors publishing numbers that can't be reproduced are the norm — not the exception.
Auth bypasses are rarely a single bug. They live in the interaction between layers — middleware, route handlers, framework annotations. Finding them requires path analysis across abstraction layers.
Chain-of-thought helps LLMs with multi-step problems. For vulnerability reasoning, it helps — but only when the chain is grounded in structured evidence.
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.
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