Output Moderation API

The foundational capability to detect and block hate speech, harassment, threats, and obscenity.

• Uses fine-tuned transformer models trained on large-scale toxicity datasets.
• Returns a hierarchical toxicity score (e.g., 0.0 to 1.0) with granular category labels.
• Example: Blocking a model output that contains a death threat while allowing a stern but safe customer service response.

Identifies when a user or downstream process is attempting to override system instructions or bypass safety guardrails.

• Scans both user inputs and model outputs for adversarial patterns.
• Detects obfuscation techniques like Base64 encoding, token smuggling, and role-playing scenarios.
• Example: Catching a 'DAN' (Do Anything Now) jailbreak attempt hidden in a user's multi-turn conversation.

Scans generated text for PII entities and either blocks the output or replaces them with placeholder tokens.

• Detects 40+ global entity types: SSNs, credit card numbers, email addresses, phone numbers, and medical record numbers.
• Supports context-aware redaction to avoid false positives on non-sensitive number sequences.
• Example: Automatically masking a customer's physical address that a model hallucinated into a product description.

Enforces compliance with industry-specific regulations by blocking prohibited content categories.

• Configurable taxonomies for finance (e.g., investment advice disclaimers), healthcare (e.g., unverified medical claims), and legal domains.
• Blocks age-restricted content and self-harm or violence glorification.
• Example: Preventing a financial chatbot from providing specific, unqualified trading recommendations to a retail investor.

Evaluates whether generated statements are factually supported by the provided context or source documents.

• Computes a grounding score by comparing output claims against a verified knowledge base.
• Flags contradictions and unsupported fabrications in real-time.
• Example: Detecting when a RAG-based support bot invents a non-existent 'premium tier' feature not found in the retrieved documentation.

Allows organizations to define and enforce proprietary content rules beyond standard safety taxonomies.

• Uses policy-as-code configurations to block competitor mentions, internal project codenames, or off-brand messaging.
• Supports regex patterns, semantic similarity thresholds, and keyword blocklists.
• Example: A media company blocking any generated text that references a specific unreleased product to prevent leaks.

The technical setup of programmable constraints that define the operational boundaries and safety limits of an AI model. Guardrails act as a policy enforcement layer, often integrating directly with the Output Moderation API to block toxic language, prevent data leakage, and enforce topical boundaries. Configurations typically include:

• Semantic filters for prohibited topics
• Regular expression patterns for PII redaction
• Threshold-based toxicity classifiers
• Jailbreak detection heuristics

A security flaw where malicious instructions override a model's system prompt, hijacking its intended behavior. Output moderation APIs serve as a last-line defense against successful injections by scanning generated text for signs of prompt leakage or unauthorized role changes. Common vectors include:

• Direct injection: 'Ignore previous instructions and...'
• Indirect injection: Poisoned data retrieved via RAG that contains hidden commands
• Multi-turn injection: Gradual manipulation across conversation turns

The degree to which a model can be manipulated to bypass its safety alignment and produce harmful content. Output moderation APIs provide runtime detection of jailbreak attempts by analyzing response semantics rather than relying solely on input filtering. Key indicators monitored include:

• Sudden shifts in compliance tone
• Generation of disallowed content categories
• Encoding-based bypasses (base64, leetspeak, ciphers)
• Role-play scenarios designed to circumvent safety training

A predefined performance boundary that a model must meet on safety benchmarks before deployment. Output moderation APIs enforce these thresholds in production by measuring real-time violation rates against acceptable limits. When thresholds are breached, automated responses include:

• Hard blocking of non-compliant outputs
• Dynamic re-prompting with safety instructions
• Escalation to human-on-the-loop review
• Automatic model rollback if violation rates spike

A document detailing findings from adversarial simulations designed to uncover safety and security flaws. These reports directly inform Output Moderation API rule sets by cataloging the specific attack patterns, toxic outputs, and edge cases that automated filters must catch. A comprehensive red-teaming exercise typically covers:

• Single-turn and multi-turn attacks
• Multilingual and cross-lingual vulnerabilities
• Domain-specific harms (medical misinformation, financial fraud)
• Adversarial suffix and prefix attacks

A standardized test suite measuring model resilience against evasion, poisoning, and other attacks. Output moderation APIs are evaluated against these benchmarks to quantify their detection accuracy and false-positive rates under adversarial conditions. Leading benchmarks include:

• HarmBench: Standardized evaluation of automated red-teaming methods
• AdvGLUE: Adversarial version of the GLUE language understanding benchmark
• ToxiGen: Large-scale machine-generated dataset for implicit hate speech detection
• RealToxicityPrompts: Measures toxicity risk across diverse prompt distributions