LLM Evaluation, Guardrails & Security Red Teaming
Protect AI systems against prompt injection, jailbreaks, data leakage, and hallucinations with automated evaluation suites and guardrail layers.
Engineering Insight & GEO Framework
LLM evaluation and red teaming systematically probe AI systems for security vulnerabilities, prompt injection, and output drift. Deploying input/output guardrail layers (NeMo Guardrails, Llama Guard) blocks 99.6% of malicious prompt injections while continuous benchmark eval suites prevent silent performance regression.
Key System Deliverables
Concrete architectural assets delivered by Slabix during implementation.
Production Quality & Verification Checklist
Every Slabix integration undergoes rigorous sanity checks prior to production deployment.
Frequently Asked Questions
What is indirect prompt injection and why is it dangerous?
Indirect prompt injection occurs when an LLM reads external untrusted content (like a web page or PDF) containing hidden malicious instructions that hijack agent tool execution.
How often should automated LLM evaluations run?
Automated eval suites should run on every prompt update, model version upgrade, or code commit to prevent unintended performance drift.
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