How do you defend enterprise LLM applications against prompt injection and jailbreak exploits?
Layer sanitization, moderation, system prompts, runtime guardrails, and post‑check to block prompt injection and jailbreaks; keep cache keys versioned.
Safety alignment, hallucination defense, moderation layers, and enterprise guardrails.
Topic Feed · System Prompts & Guardrails
Layer sanitization, moderation, system prompts, runtime guardrails, and post‑check to block prompt injection and jailbreaks; keep cache keys versioned.
Layered guardrails—system prompt, tone classifier, pre‑response PII filter, then LLM—enforce brand tone and stop data leaks.
Inject guardrails before and after LLM calls using YAML policies for NeMo and Python validators for Guardrails AI, and explicitly handle block actions.
Prompt changes alter context, so regression tests must compare logprob deltas, guardrail hits, and moderation flags across model versions.
Activate provider moderation, set probability thresholds, and layer a custom copyright classifier for comprehensive guardrails.
Real‑time guardrails add ~5‑15 ms latency and cut throughput 3‑12 %, but dramatically lower hallucination and moderation failures.
Combine immutable system prompts, OPA policy checks, and OpenAI moderation to block prompt injection and jailbreak attempts in enterprise LLM apps.
Combine deterministic prompts, live moderation, and post‑filter guardrails to keep tone consistent and stop PII leaks.