{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"wildguard","slug":"wildguard","name":"WildGuard","type":"dataset","url":"https://github.com/allenai/wildguard","page_url":"https://unfragile.ai/wildguard","categories":["model-training"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"wildguard__cap_0","uri":"capability://safety.moderation.multi.class.prompt.harmfulness.classification","name":"multi-class prompt harmfulness classification","description":"Classifies incoming prompts across multiple harm categories (e.g., violence, illegal activity, sexual content, hate speech, self-harm) using a fine-tuned language model trained on diverse adversarial examples. The model learns to recognize harmful intent patterns across different risk domains simultaneously, enabling nuanced detection beyond binary safe/unsafe classification. Outputs confidence scores per harm category to support downstream risk-based routing decisions.","intents":["detect if a user prompt is attempting to elicit harmful content before it reaches the main LLM","classify the type of harm a prompt is attempting to trigger for logging and analysis","route suspicious prompts to human review queues based on harm category confidence thresholds","build safety dashboards that track distribution of attempted harms across user populations"],"best_for":["LLM application builders implementing safety gates at inference time","security teams monitoring for adversarial prompt patterns","researchers studying jailbreak techniques and prompt injection attacks"],"limitations":["Classification accuracy varies by harm category — some edge cases (subtle manipulation, cultural context) may be misclassified","Model trained on English-language prompts; cross-lingual generalization not guaranteed","Requires inference latency (~50-200ms per prompt depending on model size) that must be budgeted into request pipelines","Cannot detect novel harm categories outside training distribution without retraining"],"requires":["Python 3.8+","PyTorch or TensorFlow for model inference","Pre-trained WildGuard model weights (downloadable from HuggingFace Hub)","GPU recommended for production throughput (CPU inference viable for <100 req/sec)"],"input_types":["text (raw user prompts, variable length up to model context window)"],"output_types":["structured JSON with per-category harm scores (0.0-1.0 confidence)","categorical label (safe/unsafe with primary harm type)","confidence threshold metadata for decision-making"],"categories":["safety-moderation","text-classification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"wildguard__cap_1","uri":"capability://safety.moderation.response.harmfulness.detection.and.classification","name":"response harmfulness detection and classification","description":"Analyzes LLM-generated responses to identify harmful content that slipped past prompt filtering, classifying violations across the same harm taxonomy as prompt detection. Uses a separate classification head trained on model outputs paired with human safety judgments, enabling detection of harmful content generation even when the initial prompt appeared benign. Supports both full-response analysis and streaming token-level detection for real-time filtering.","intents":["catch harmful LLM outputs before they reach end users, even if the prompt passed initial safety checks","classify what type of harm the model generated (violence, illegal advice, etc.) for safety incident analysis","implement guardrails that block or truncate responses exceeding harm thresholds before delivery","audit model behavior to identify systematic safety failures in specific domains"],"best_for":["production LLM applications requiring output-level safety gates","teams conducting red-teaming and safety audits of model behavior","researchers analyzing failure modes in instruction-tuned models"],"limitations":["Detection latency adds ~100-300ms per response, creating tension with user experience expectations","Cannot distinguish between harmful content and legitimate discussion of harmful topics (e.g., educational content about cybersecurity)","Requires full response text; streaming detection may miss context from later tokens","False positive rate increases on edge cases like satire, fiction, or hypothetical scenarios"],"requires":["Python 3.8+","PyTorch or TensorFlow","WildGuard response classifier model weights","Full LLM response text (or sufficient context window for streaming analysis)"],"input_types":["text (LLM-generated responses, typically 100-2000 tokens)"],"output_types":["structured JSON with per-category harm scores for response content","binary safe/unsafe classification with primary harm category","token-level confidence scores for streaming implementations"],"categories":["safety-moderation","text-classification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"wildguard__cap_2","uri":"capability://safety.moderation.refusal.detection.and.classification","name":"refusal detection and classification","description":"Identifies when an LLM refuses to answer a prompt and classifies the refusal reason (safety concern, capability limitation, policy violation, etc.) using a specialized classifier trained on refusal patterns. This enables distinguishing between legitimate refusals (model correctly declining harmful requests) and false refusals (model unnecessarily blocking benign requests), supporting both safety auditing and user experience optimization. Outputs refusal confidence and category to enable downstream handling (e.g., rephrasing suggestions, escalation).","intents":["measure false refusal rates to identify over-cautious model behavior degrading user experience","validate that refusals are appropriately targeted at harmful requests rather than benign ones","categorize refusal reasons for safety incident analysis and model behavior auditing","implement workflows that suggest prompt rephrasing when refusals are overly broad"],"best_for":["teams optimizing model safety policies to minimize false refusals","researchers studying model alignment and refusal behavior","product teams analyzing user friction from over-cautious safety guardrails"],"limitations":["Requires labeled training data on refusal patterns; generalization to novel refusal phrasings may be limited","Cannot distinguish between intentional refusals and accidental model failures without additional context","Refusal detection accuracy depends on model's refusal verbosity — terse refusals may be harder to classify","Does not provide guidance on whether a refusal was appropriate, only what was refused"],"requires":["Python 3.8+","PyTorch or TensorFlow","WildGuard refusal classifier model weights","LLM response text (ideally with conversation context for accuracy)"],"input_types":["text (LLM responses containing refusal statements)"],"output_types":["structured JSON with refusal confidence score (0.0-1.0)","refusal category label (safety-based, capability-based, policy-based, etc.)","metadata on refusal reason for logging and analysis"],"categories":["safety-moderation","text-classification"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"wildguard__cap_3","uri":"capability://data.processing.analysis.curated.adversarial.prompt.dataset.with.human.annotations","name":"curated adversarial prompt dataset with human annotations","description":"Provides a structured dataset of 10K+ adversarial prompts spanning 13 harm categories, each annotated by human raters for prompt harmfulness, response harmfulness, and refusal appropriateness. The dataset includes diverse attack patterns (jailbreaks, prompt injections, social engineering) and edge cases, enabling researchers and builders to train, evaluate, and benchmark safety classifiers. Supports both supervised fine-tuning of safety models and evaluation of existing LLM safety mechanisms.","intents":["train custom safety classifiers on adversarial prompt patterns specific to your application domain","benchmark existing LLM safety mechanisms against a standardized adversarial dataset","analyze failure modes in safety systems by examining misclassified examples","conduct red-teaming and adversarial robustness testing of LLM applications"],"best_for":["researchers developing safety classification models","security teams building custom safety systems for proprietary LLMs","organizations conducting comprehensive safety audits of LLM deployments"],"limitations":["Dataset is English-language only; non-English adversarial patterns not represented","Human annotation quality varies; inter-rater agreement metrics should be consulted for sensitive use cases","Dataset size (10K examples) may be insufficient for training robust classifiers on all 13 harm categories equally","Adversarial patterns in dataset may become outdated as attack techniques evolve; requires periodic updates"],"requires":["Python 3.8+ for dataset loading and processing","Hugging Face datasets library for convenient access","Sufficient disk space (~500MB-1GB depending on format)","Understanding of multi-label classification and imbalanced datasets"],"input_types":["structured dataset (JSON, Parquet, or CSV format with prompt text and annotations)"],"output_types":["prompt text (variable length, up to ~2000 tokens)","harm category labels (multi-label, 13 categories)","human annotation scores (confidence, inter-rater agreement metrics)","metadata (attack type, difficulty level, etc.)"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"wildguard__cap_4","uri":"capability://safety.moderation.pre.trained.safety.classifier.model.with.multi.task.learning","name":"pre-trained safety classifier model with multi-task learning","description":"Provides a fine-tuned language model (based on Llama 2 or similar backbone) trained via multi-task learning to simultaneously predict prompt harmfulness, response harmfulness, and refusal appropriateness. The model uses shared representations for all three tasks, enabling efficient inference and transfer learning across safety dimensions. Available in multiple sizes (7B, 13B parameters) to support different latency/accuracy trade-offs in production deployments.","intents":["deploy a pre-trained safety classifier without training from scratch, reducing time-to-production","leverage transfer learning to fine-tune the model on domain-specific safety concerns with minimal labeled data","run safety classification on-device or in private infrastructure without sending prompts to external APIs","benchmark model safety against a standardized classifier to identify systematic vulnerabilities"],"best_for":["teams building safety-critical LLM applications with limited ML expertise","organizations requiring on-premise safety classification for data privacy","researchers studying multi-task learning for safety classification"],"limitations":["Model size (7B-13B parameters) requires GPU for reasonable inference latency; CPU inference is impractical for production","Multi-task learning introduces trade-offs — optimizing for all three tasks simultaneously may reduce accuracy on individual tasks vs. single-task models","Model trained on English adversarial patterns; cross-lingual transfer not validated","Requires fine-tuning on domain-specific data to achieve optimal accuracy for specialized applications"],"requires":["Python 3.8+","PyTorch 2.0+ or TensorFlow 2.10+","GPU with 16GB+ VRAM (for 7B model) or 24GB+ (for 13B model)","Hugging Face transformers library (version 4.30+)","Model weights downloadable from Hugging Face Hub"],"input_types":["text (prompts or responses, up to model context window)"],"output_types":["structured JSON with three prediction heads: prompt_harm_scores, response_harm_scores, refusal_appropriateness_score","per-category confidence scores (0.0-1.0 for each of 13 harm categories)","aggregated safety decision (safe/unsafe) with confidence threshold"],"categories":["safety-moderation","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"wildguard__cap_5","uri":"capability://safety.moderation.harm.category.taxonomy.and.annotation.schema","name":"harm category taxonomy and annotation schema","description":"Defines a structured taxonomy of 13 harm categories (violence, illegal activity, sexual content, hate speech, self-harm, etc.) with clear definitions and annotation guidelines for consistent human labeling. The schema supports multi-label annotation (a single prompt can belong to multiple categories) and confidence scoring, enabling nuanced safety classification beyond binary safe/unsafe decisions. Includes inter-rater agreement metrics and quality control procedures for maintaining annotation consistency.","intents":["standardize harm classification across different safety systems and teams using a common taxonomy","train human annotators on consistent harm definitions to ensure high-quality labeled data","evaluate safety systems using a structured rubric that captures multiple harm dimensions","communicate safety decisions to users and stakeholders using clear, standardized harm categories"],"best_for":["organizations building internal safety datasets and annotation workflows","teams standardizing safety definitions across multiple LLM applications","researchers studying harm taxonomy design and annotation methodology"],"limitations":["Taxonomy is English-language focused; cultural context and non-English harm concepts may not be fully represented","13 categories may be too granular for some applications or too coarse for others; customization required","Annotation schema requires training and calibration; inter-rater agreement may be low on ambiguous edge cases","Taxonomy may become outdated as new harm patterns emerge (e.g., deepfakes, AI-specific harms)"],"requires":["Clear documentation of harm category definitions","Annotation tool supporting multi-label classification (e.g., Prodigy, Label Studio)","Human annotators trained on taxonomy and guidelines","Quality control procedures (e.g., inter-rater agreement checks, expert review)"],"input_types":["harm category definitions (text descriptions)","annotation guidelines (text with examples)"],"output_types":["structured annotation schema (JSON or YAML format)","labeled dataset with multi-label harm categories","inter-rater agreement metrics (Cohen's kappa, Fleiss' kappa)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"wildguard__cap_6","uri":"capability://safety.moderation.evaluation.benchmark.for.safety.classifier.performance","name":"evaluation benchmark for safety classifier performance","description":"Provides standardized evaluation metrics and benchmark results for safety classifiers, including precision, recall, F1-score, and ROC-AUC across all 13 harm categories. Enables comparison of different safety approaches (API-based, fine-tuned models, rule-based systems) on a common test set with consistent evaluation methodology. Includes ablation studies showing the contribution of different training techniques (multi-task learning, data augmentation, etc.) to overall performance.","intents":["compare the performance of different safety classification approaches on a standardized benchmark","identify which harm categories are harder to detect and require additional training data or techniques","validate that a custom safety classifier meets minimum accuracy thresholds before production deployment","measure the impact of model improvements (e.g., fine-tuning on domain-specific data) on safety performance"],"best_for":["researchers developing new safety classification techniques","teams evaluating commercial vs. open-source safety solutions","organizations conducting safety audits and compliance verification"],"limitations":["Benchmark results are specific to the test set; performance on out-of-distribution adversarial examples may differ significantly","Evaluation metrics (precision, recall) may not capture all aspects of safety (e.g., false positive impact on user experience)","Benchmark does not account for latency, throughput, or cost trade-offs between different approaches","Results may become outdated as new attack techniques emerge that are not represented in the test set"],"requires":["Test dataset with ground truth labels (provided by WildGuard)","Safety classifier implementation to evaluate","Evaluation script or framework (e.g., scikit-learn, PyTorch Lightning)","Sufficient compute for running inference on full test set"],"input_types":["safety classifier predictions (confidence scores or labels)","ground truth labels (multi-label harm categories)"],"output_types":["structured evaluation metrics (JSON with precision, recall, F1, ROC-AUC per category)","confusion matrices and per-category performance breakdowns","benchmark comparison table vs. baseline and alternative approaches"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"wildguard__cap_7","uri":"capability://code.generation.editing.fine.tuning.framework.for.domain.specific.safety.models","name":"fine-tuning framework for domain-specific safety models","description":"Provides training scripts, loss functions, and hyperparameter configurations for fine-tuning the WildGuard base model on domain-specific safety concerns with minimal labeled data. Implements techniques like low-rank adaptation (LoRA), data augmentation, and curriculum learning to improve sample efficiency and reduce overfitting. Includes evaluation utilities for monitoring validation performance and early stopping to prevent degradation on the original safety tasks.","intents":["adapt the pre-trained safety model to domain-specific harm patterns (e.g., financial fraud, medical misinformation) with limited labeled examples","reduce fine-tuning time and computational cost using parameter-efficient techniques like LoRA","maintain safety performance on the original 13 harm categories while adding domain-specific detection","experiment with different training configurations to find optimal accuracy/latency trade-offs"],"best_for":["teams building safety classifiers for specialized domains (finance, healthcare, legal)","organizations with limited ML expertise wanting to customize safety models","researchers studying transfer learning and domain adaptation for safety"],"limitations":["Fine-tuning requires labeled domain-specific data; quality and quantity of labels directly impact performance","Parameter-efficient techniques (LoRA) may reduce model capacity for complex domain-specific patterns","Fine-tuning can cause catastrophic forgetting of original safety knowledge; careful regularization required","Hyperparameter tuning is necessary for optimal performance; no single configuration works for all domains"],"requires":["Python 3.8+","PyTorch 2.0+ with CUDA support","GPU with 16GB+ VRAM for efficient fine-tuning","Labeled domain-specific dataset (minimum 100-500 examples recommended)","Hugging Face transformers and PEFT libraries"],"input_types":["domain-specific prompts or responses (text)","harm labels for domain-specific categories (multi-label)","hyperparameter configuration (JSON or YAML)"],"output_types":["fine-tuned model weights (saved as PyTorch checkpoints or Hugging Face format)","training logs with loss curves and validation metrics","evaluation report comparing fine-tuned model to baseline"],"categories":["code-generation-editing","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"wildguard__cap_8","uri":"capability://tool.use.integration.integration.with.llm.inference.frameworks.and.apis","name":"integration with llm inference frameworks and apis","description":"Provides pre-built integrations with popular LLM inference frameworks (vLLM, TGI, Ollama) and API providers (OpenAI, Anthropic, Together AI) to enable seamless safety classification in production pipelines. Supports both synchronous and asynchronous inference, batching for throughput optimization, and caching to reduce redundant classifications. Includes middleware for intercepting prompts/responses and applying safety filters before delivery to users.","intents":["add safety classification to existing LLM applications without major architectural changes","batch safety checks across multiple prompts to improve throughput and reduce latency","cache safety classifications to avoid redundant inference on repeated prompts","implement safety gates that block or modify responses based on classification results"],"best_for":["teams integrating safety into production LLM applications","organizations using multiple LLM providers and needing unified safety layer","developers building LLM agents and applications with safety requirements"],"limitations":["Integration complexity varies by framework; some frameworks may require custom adapters","Batching and caching add complexity to request handling; careful design needed to avoid race conditions","Safety classification latency (100-300ms) must be budgeted into overall request latency","Caching introduces stale classification risk if safety policies change frequently"],"requires":["Python 3.8+","Target LLM inference framework or API client library","WildGuard model weights or API access","Async/await support for non-blocking safety checks (Python 3.7+)"],"input_types":["prompts and responses from LLM inference pipeline (text)","configuration for safety thresholds and filtering behavior (JSON)"],"output_types":["safety classification results (JSON with harm scores)","filtered or modified responses (text)","safety decision (allow/block/modify) for downstream handling"],"categories":["tool-use-integration","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"wildguard__headline","uri":"capability://safety.moderation.safety.classification.model.for.detecting.harmful.prompts.and.responses","name":"safety classification model for detecting harmful prompts and responses","description":"WildGuard is a comprehensive safety classification dataset and model designed to accurately detect harmful prompts and responses across various risk scenarios, making it essential for developers focused on AI safety.","intents":["best safety classification model","safety dataset for harmful prompt detection","AI model for response harmfulness detection","dataset for refusal detection in AI","how to classify harmful prompts in AI"],"best_for":["AI safety researchers","developers building safety features"],"limitations":[],"requires":[],"input_types":["text prompts"],"output_types":["classification labels"],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":56,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch or TensorFlow for model inference","Pre-trained WildGuard model weights (downloadable from HuggingFace Hub)","GPU recommended for production throughput (CPU inference viable for <100 req/sec)","PyTorch or TensorFlow","WildGuard response classifier model weights","Full LLM response text (or sufficient context window for streaming analysis)","WildGuard refusal classifier model weights","LLM response text (ideally with conversation context for accuracy)","Python 3.8+ for dataset loading and processing"],"failure_modes":["Classification accuracy varies by harm category — some edge cases (subtle manipulation, cultural context) may be misclassified","Model trained on English-language prompts; 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