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The model performs multi-label classification with confidence scores, enabling downstream filtering decisions. Unlike generic classifiers, it reasons about context and intent rather than pattern-matching keywords, reducing false positives on sarcasm, reclaimed language, and domain-specific terminology.","intents":["I need to filter user-generated content in my platform before it reaches other users","I want to classify incoming prompts to detect jailbreak attempts or adversarial inputs before they reach my main LLM","I need to audit historical content in my database for policy violations with explainable reasoning"],"best_for":["platform teams building content moderation pipelines","LLM application builders protecting against adversarial prompts","compliance teams needing explainable safety decisions"],"limitations":["MoE architecture introduces variable latency (50-200ms) depending on which experts activate for a given input","Trained on English-centric safety data; performance degrades on non-English content and code-mixed text","Classification confidence scores reflect training data distribution, not true uncertainty — may be overconfident on out-of-distribution inputs","No real-time streaming support; requires full text input before classification begins"],"requires":["OpenAI API key or OpenRouter API key","Network connectivity to OpenAI endpoints","Text input under 8,000 tokens (approximate context window)"],"input_types":["text (plain text, user messages, prompts, content snippets)"],"output_types":["structured JSON with classification labels and confidence scores","reasoning explanation (if requested)"],"categories":["safety-moderation","content-classification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-oss-safeguard-20b__cap_1","uri":"capability://safety.moderation.adversarial.prompt.detection.and.jailbreak.filtering","name":"adversarial prompt detection and jailbreak filtering","description":"Detects and flags adversarial prompts, jailbreak attempts, and prompt injection attacks by analyzing linguistic patterns, instruction-following cues, and known attack vectors. The model identifies attempts to override system instructions, bypass safety guidelines, or manipulate the LLM into unsafe behavior. It operates as a gating layer that can reject or flag suspicious inputs before they reach downstream LLMs, reducing attack surface.","intents":["I want to block jailbreak attempts before they reach my production LLM","I need to detect prompt injection attacks in user inputs to my chatbot","I want to identify when users are trying to manipulate my system into generating unsafe content"],"best_for":["LLM application developers building secure inference pipelines","security teams protecting against prompt-based attacks","teams running multi-turn conversations with untrusted users"],"limitations":["Adversarial detection is an arms race; new attack patterns may evade detection until the model is retrained","False positive rate increases on legitimate edge cases (creative writing, educational content about attacks, security research)","Cannot detect attacks that are semantically valid but contextually harmful (e.g., requests for help with illegal activities phrased as hypotheticals)","Requires integration at inference time; cannot retroactively audit historical conversations"],"requires":["OpenAI API key or OpenRouter API key","Integration point before main LLM inference (middleware or preprocessing layer)","Decision logic for handling flagged inputs (reject, quarantine, or log)"],"input_types":["text (user prompts, multi-turn conversation history)"],"output_types":["boolean flag (is_adversarial: true/false)","confidence score","attack type classification (jailbreak, injection, manipulation, etc.)"],"categories":["safety-moderation","adversarial-detection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-oss-safeguard-20b__cap_2","uri":"capability://safety.moderation.llm.output.filtering.and.safety.validation","name":"llm output filtering and safety validation","description":"Validates and filters text generated by downstream LLMs before it reaches users, detecting unsafe, harmful, or policy-violating outputs. The model analyzes generated text for toxicity, misinformation, privacy violations, and other safety concerns, enabling post-hoc filtering of LLM outputs. It can be integrated as a guardrail layer in inference pipelines to prevent unsafe content from being served.","intents":["I want to catch unsafe outputs from my LLM before they reach users","I need to filter generated content for policy violations in real-time","I want to validate that my LLM is following safety guidelines in production"],"best_for":["teams running production LLM services with safety requirements","platforms with content policies that need automated enforcement","developers building multi-stage inference pipelines with safety gates"],"limitations":["Post-hoc filtering cannot prevent unsafe reasoning inside the LLM; it only catches outputs after generation (adds latency and compute cost)","May reject legitimate outputs that trigger safety heuristics (e.g., educational content about harmful topics, fiction with dark themes)","Requires tuning of confidence thresholds per use case; no one-size-fits-all setting","Cannot understand nuanced context like satire, academic discussion, or harm reduction advice"],"requires":["OpenAI API key or OpenRouter API key","Integration point after LLM generation but before user-facing output","Fallback strategy for rejected outputs (regenerate, return error, use safe default)"],"input_types":["text (LLM-generated outputs, completions, responses)"],"output_types":["safety verdict (safe/unsafe)","confidence score","violation category (toxicity, misinformation, privacy, etc.)","optional: suggested edits or safe alternative"],"categories":["safety-moderation","output-validation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-oss-safeguard-20b__cap_3","uri":"capability://safety.moderation.multi.label.safety.classification.with.confidence.scoring","name":"multi-label safety classification with confidence scoring","description":"Performs simultaneous classification across multiple safety dimensions (toxicity, hate speech, sexual content, violence, illegal activity, misinformation, privacy violations, etc.) with independent confidence scores for each label. The model outputs a structured safety profile rather than a single binary decision, enabling fine-grained policy enforcement. Each label is scored independently, allowing downstream systems to apply different thresholds per category.","intents":["I need to classify content across multiple safety dimensions to apply category-specific policies","I want to understand which specific safety concerns apply to a piece of content, not just whether it's safe or unsafe","I need to tune my moderation system differently for different types of harm (e.g., stricter on violence, more lenient on mild language)"],"best_for":["platforms with nuanced content policies that vary by harm category","teams building configurable moderation systems","compliance teams needing detailed safety audit trails"],"limitations":["Multi-label classification can produce contradictory or overlapping labels (e.g., content flagged as both 'misinformation' and 'satire')","Confidence scores are calibrated on training data distribution; may not reflect true uncertainty on novel content types","Requires interpretation of multiple thresholds; no single 'safe' decision point","Label definitions may not align with your specific policy (e.g., 'hate speech' definition varies by jurisdiction)"],"requires":["OpenAI API key or OpenRouter API key","Logic to interpret and combine multiple labels into policy decisions","Threshold tuning per label based on your use case and risk tolerance"],"input_types":["text (content to classify)"],"output_types":["structured JSON with multiple labels and confidence scores","example: { 'toxicity': 0.92, 'hate_speech': 0.15, 'sexual_content': 0.03, 'violence': 0.78 }"],"categories":["safety-moderation","multi-label-classification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-oss-safeguard-20b__cap_4","uri":"capability://safety.moderation.low.latency.safety.inference.with.sparse.moe.activation","name":"low-latency safety inference with sparse moe activation","description":"Achieves sub-200ms latency for safety classification by using Mixture-of-Experts (MoE) architecture with sparse activation. Rather than running all 21B parameters, the model routes each input through a gating network that selects only the relevant expert subnetworks (typically 2-4 experts out of many), reducing compute by 80-90%. This enables real-time safety filtering in high-throughput systems without dedicated GPU infrastructure.","intents":["I need to add safety filtering to my API without adding significant latency","I want to run safety checks on every user input in real-time without slowing down my system","I need to scale safety moderation to handle millions of requests per day cost-effectively"],"best_for":["high-throughput platforms (social media, messaging, content platforms)","teams with strict latency SLAs (p99 < 200ms)","cost-sensitive deployments where per-request inference cost matters"],"limitations":["MoE latency is variable; some inputs trigger more experts than others, causing p99 tail latency to be higher than p50","Sparse activation means some safety concerns may be missed if they don't trigger the relevant experts (lower recall than dense models)","Requires careful tuning of gating network to balance latency vs. accuracy; aggressive sparsity hurts safety performance","OpenRouter/API-based deployment adds network latency (50-100ms) on top of model inference"],"requires":["OpenAI API key or OpenRouter API key","Acceptance of variable latency (p50 ~50ms, p99 ~150-200ms)","Batch processing capability to amortize API overhead"],"input_types":["text (any length up to context window)"],"output_types":["safety classification (same as other capabilities)","latency metadata (optional)"],"categories":["safety-moderation","performance-optimization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-openai-gpt-oss-safeguard-20b__cap_5","uri":"capability://safety.moderation.context.aware.safety.reasoning.with.semantic.understanding","name":"context-aware safety reasoning with semantic understanding","description":"Evaluates safety by understanding semantic context, intent, and nuance rather than pattern-matching keywords. The model reasons about whether content is harmful in context (e.g., distinguishing between reclaimed language, educational discussion of harmful topics, and actual harm). It uses transformer-based attention mechanisms to weigh different parts of the input, understanding that the same phrase can be safe or unsafe depending on context.","intents":["I want to reduce false positives from my safety system on legitimate edge cases like satire, dark humor, and educational content","I need to understand why my safety system flagged something, not just get a binary decision","I want to handle code-mixed and multilingual content more accurately in safety classification"],"best_for":["platforms with diverse user bases and cultural contexts","teams building safety systems that need to explain decisions to users","applications where false positives are costly (e.g., educational platforms, research communities)"],"limitations":["Semantic reasoning is slower than keyword matching; latency increases with input length","Context understanding is imperfect; the model can still misinterpret sarcasm, cultural references, and novel linguistic patterns","Reasoning is opaque; 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