prompt moderation

**Prompt moderation** is the **pre-inference safety process that evaluates user prompts for harmful intent, policy violations, or attack patterns before model execution** - it reduces exposure by blocking risky inputs early in the pipeline. **What Is Prompt moderation?** - **Definition**: Input-side moderation focused on classifying prompt risk and deciding whether generation should proceed. - **Detection Scope**: Harmful requests, self-harm intent, abuse content, injection attempts, and suspicious obfuscation. - **Decision Actions**: Allow, refuse, request clarification, throttle, or escalate for human review. - **System Integration**: Works with rate limits, user trust scores, and guardrail policy engines. **Why Prompt moderation Matters** - **Prevention First**: Stops high-risk requests before they reach generation models. - **Safety Efficiency**: Reduces downstream moderation load and unsafe response incidents. - **Abuse Mitigation**: Helps detect repeated adversarial behavior and coordinated attack traffic. - **Operational Control**: Supports adaptive enforcement based on user behavior history. - **Compliance Assurance**: Demonstrates proactive risk handling in AI governance frameworks. **How It Is Used in Practice** - **Risk Scoring**: Combine category classifiers with heuristic attack-pattern signals. - **Policy Routing**: Apply tiered actions by severity, confidence, and user trust context. - **Feedback Loop**: Use moderation outcomes to improve rules, models, and abuse detection systems. Prompt moderation is **a critical front-line defense in LLM safety architecture** - early input screening materially reduces misuse risk and improves reliability of downstream model behavior.