What can WildGuard do?

multi-class prompt harmfulness classification, response-level harm detection and classification, refusal detection and compliance scoring, adversarial dataset curation and annotation, multi-model safety evaluation and benchmarking, fine-tuning and custom classifier training, harm category taxonomy and schema definition

WildGuard

DatasetFree

Allen AI's safety classification dataset and model.

Open Source

/ 100

7 capabilities

Capabilities7 decomposed

multi-class prompt harmfulness classification

Medium confidence

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, jailbreak attempts, and context-dependent risks through supervised learning on the WildGuard dataset, enabling real-time triage of user inputs before they reach downstream systems.

Solves for

detect jailbreak attempts and adversarial prompts before they reach my LLMclassify incoming user requests by harm category to route to appropriate moderation workflowsidentify subtle or indirect harmful requests that simple keyword filtering would missmeasure prompt safety across different user segments and use cases

Best for

LLM application developers building safety layers for public-facing chatbots

teams deploying multi-tenant AI systems requiring per-user risk assessment

security researchers evaluating adversarial robustness of AI systems

Requires

Python 3.8+

PyTorch 1.9+ or TensorFlow 2.6+

Pre-trained WildGuard model weights (available via HuggingFace or GitHub)

Limitations

Classification accuracy varies by harm category — some edge cases (e.g., implicit threats) require human review

Model trained primarily on English text; cross-lingual performance not fully characterized

Requires inference latency of ~50-200ms per prompt depending on model size and hardware

What makes it unique

WildGuard's prompt classifier is trained on a diverse, adversarially-curated dataset spanning 10+ harm categories and 100+ attack patterns, enabling detection of subtle jailbreaks and context-dependent harms that rule-based systems miss. The dataset includes both naturally-occurring harmful prompts and synthetically-generated adversarial examples, providing coverage of emerging attack vectors.

vs alternatives

Outperforms OpenAI's moderation API and Perspective API on adversarial prompt detection due to exposure to jailbreak-specific training data and multi-category granularity, though requires self-hosting for latency-sensitive applications.

response-level harm detection and classification

Medium confidence

Analyzes LLM-generated responses to classify whether they contain harmful content, even if the original prompt was benign. The model evaluates response text against the same multi-category harm taxonomy (violence, illegal, sexual, hate, self-harm) using fine-tuned classification layers, enabling detection of model failures, prompt injection attacks, or jailbreak successes that bypass prompt-level filters.

Solves for

catch harmful model outputs before they reach users, even if the input prompt passed safety checksdetect when an LLM has been successfully jailbroken or manipulated into generating harmful contentaudit historical conversations for harmful responses to identify systemic safety issuesimplement multi-stage safety pipelines with both input and output filtering

Best for

production LLM systems requiring defense-in-depth safety architecture

teams conducting red-teaming or adversarial testing of LLM outputs

compliance-heavy industries (finance, healthcare, legal) needing output audit trails

Requires

Python 3.8+

PyTorch 1.9+ or TensorFlow 2.6+

WildGuard response classifier model weights

Limitations

Response classification is context-dependent — same text may be safe in educational context but harmful in other contexts; model has limited context window

Inference latency adds ~100-300ms per response, impacting real-time streaming use cases

False positives on sarcasm, satire, or educational content about harmful topics (e.g., explaining how to recognize scams)

What makes it unique

WildGuard's response classifier is specifically trained to detect harmful outputs from LLMs, including subtle failures like partial compliance with harmful requests, indirect harm (e.g., providing information that enables harm), and context-dependent violations. The training data includes both human-written harmful responses and LLM-generated failures, capturing model-specific failure modes.

vs alternatives

More effective than generic content filters (e.g., regex-based keyword matching) at detecting LLM-specific failure modes and indirect harms, and more efficient than human review for high-volume systems, though requires integration into inference pipelines.

refusal detection and compliance scoring

Medium confidence

Evaluates whether an LLM's response appropriately refuses a harmful request, measuring both the presence of refusal and its quality/completeness. The model classifies responses into categories like 'appropriate refusal', 'partial refusal', 'no refusal', and 'harmful compliance', enabling assessment of whether safety training is working and identifying cases where models fail to refuse harmful requests.

Solves for

measure whether my LLM is properly refusing harmful requests as intendedidentify failure modes where the model complies with harmful requests instead of refusingaudit safety training effectiveness across different harm categories and attack vectorsdetect partial refusals that provide some harmful information despite claiming to refuse

Best for

LLM developers evaluating safety training and alignment techniques

teams conducting systematic red-teaming to measure refusal robustness

researchers studying jailbreak resistance and adversarial robustness

Requires

Python 3.8+

PyTorch 1.9+ or TensorFlow 2.6+

WildGuard refusal detection model

Limitations

Refusal quality is subjective — model may disagree with human judgment on what constitutes 'appropriate' refusal

Requires paired data (harmful prompt + response) for meaningful evaluation; cannot assess refusal in isolation

Does not evaluate whether refusals are helpful or provide constructive alternatives

What makes it unique

WildGuard's refusal detector goes beyond binary 'refused/complied' classification to measure refusal quality and identify partial compliance cases where models provide some harmful information while claiming to refuse. This enables fine-grained assessment of safety training effectiveness and detection of sophisticated jailbreaks that partially succeed.

vs alternatives

More nuanced than simple compliance detection (which only checks if harmful content was generated) because it evaluates whether refusals are appropriate and complete, enabling measurement of safety training quality rather than just binary safety outcomes.

adversarial dataset curation and annotation

Medium confidence

Provides a curated, multi-category dataset of harmful prompts, benign prompts, and LLM responses with human annotations for harm classification and refusal quality. The dataset includes naturally-occurring harmful requests, synthetically-generated adversarial examples, jailbreak attempts, and edge cases, enabling training and evaluation of safety classifiers. Data is structured with category labels, confidence scores, and metadata for systematic safety research.

Solves for

train custom safety classifiers fine-tuned to my specific use case and harm taxonomyevaluate baseline safety classifier performance on diverse adversarial examplesconduct systematic red-teaming by accessing a comprehensive library of known attack patternsbenchmark my LLM's safety against a standardized, peer-reviewed dataset

Best for

researchers developing new safety techniques and alignment methods

teams building domain-specific safety classifiers (e.g., for medical or financial LLMs)

organizations conducting systematic adversarial testing and red-teaming

Requires

Python 3.8+ for dataset loading and processing

HuggingFace datasets library or direct GitHub access

Sufficient storage for full dataset (~1-5GB depending on format)

Limitations

Dataset is primarily English-language; limited coverage of non-English harm patterns and cultural context

Annotations reflect human judgment which may not align with all use cases or cultural norms

Dataset size is finite (~10k-100k examples depending on category); may not cover all emerging attack vectors

What makes it unique

WildGuard dataset combines naturally-occurring harmful prompts from real-world sources with synthetically-generated adversarial examples and jailbreak attempts, providing comprehensive coverage of both known attack patterns and edge cases. The dataset includes multi-level annotations (harm category, severity, refusal quality) enabling fine-grained analysis and training of nuanced safety models.

vs alternatives

More comprehensive and adversarially-focused than generic text classification datasets, and more systematically curated than ad-hoc red-teaming examples, providing a standardized benchmark for safety research that enables reproducible evaluation across teams.

multi-model safety evaluation and benchmarking

Medium confidence

Enables systematic evaluation of different LLMs' safety performance by running WildGuard classifiers against model outputs on the same adversarial prompt set, generating comparative safety metrics across models, harm categories, and attack types. Produces structured evaluation reports with per-category performance, refusal rates, and failure mode analysis, enabling data-driven model selection and safety comparison.

Solves for

compare safety performance across different LLM providers (OpenAI, Anthropic, Meta, etc.)measure how safety performance varies across harm categories for a given modelidentify which attack types are most effective against specific modelstrack safety improvements over model versions and fine-tuning iterations

Best for

teams evaluating LLM providers for production deployment with safety requirements

researchers conducting comparative studies of LLM safety and alignment

organizations tracking safety metrics over time as models are updated

Requires

Python 3.8+

API keys or local access to LLMs being evaluated

WildGuard classifier models

Limitations

Evaluation is only as good as the WildGuard classifier — systematic biases in the classifier will skew comparisons

Requires running inference on all models being compared, which is expensive and time-consuming for large-scale evaluation

Results are specific to the WildGuard dataset and taxonomy; may not generalize to other harm definitions or use cases

What makes it unique

WildGuard enables standardized, reproducible safety evaluation across different LLMs using a consistent classifier and dataset, allowing fair comparison of safety performance independent of each model's built-in safety mechanisms. The evaluation framework captures both refusal behavior and response-level harm, providing multi-dimensional safety assessment.

vs alternatives

More systematic and reproducible than manual red-teaming or ad-hoc safety testing, and more comprehensive than single-metric safety scores because it breaks down performance by harm category and attack type, enabling nuanced model selection decisions.

fine-tuning and custom classifier training

Medium confidence

Provides pre-trained model weights and training infrastructure enabling teams to fine-tune WildGuard classifiers on custom datasets or domain-specific harm taxonomies. Supports transfer learning from the base WildGuard models to adapt safety classification to specialized use cases (e.g., medical, financial, legal domains) with minimal labeled data, using standard PyTorch/TensorFlow training loops and HuggingFace integration.

Solves for

adapt WildGuard to my domain-specific harm definitions and risk categoriestrain a safety classifier on my proprietary data without sharing it with external APIsimprove classification accuracy for my specific use case by fine-tuning on domain examplesbuild a custom safety classifier with lower latency or smaller model size for edge deployment

Best for

teams with domain-specific safety requirements (medical, financial, legal, etc.)

organizations with proprietary data that cannot be shared with external safety services

developers building edge-deployed safety systems with latency/size constraints

Requires

Python 3.8+

PyTorch 1.9+ or TensorFlow 2.6+

HuggingFace transformers library

Limitations

Fine-tuning requires labeled training data; quality and quantity directly impact classifier performance

Transfer learning effectiveness depends on similarity between WildGuard training data and target domain

Fine-tuned models may overfit to specific domains and perform poorly on out-of-distribution examples

What makes it unique

WildGuard provides open-source pre-trained weights and training code enabling straightforward fine-tuning on custom datasets, with HuggingFace integration reducing boilerplate. The base models are trained on diverse adversarial examples, providing strong transfer learning initialization for domain-specific safety tasks.

vs alternatives

More flexible than closed-source safety APIs (which cannot be customized) and more efficient than training safety classifiers from scratch, because transfer learning from WildGuard's adversarially-trained base models requires less labeled data and converges faster.

harm category taxonomy and schema definition

Medium confidence

Defines a structured, multi-level harm taxonomy covering 10+ primary categories (violence, illegal activity, sexual content, hate speech, self-harm, etc.) with sub-categories and severity levels. The taxonomy is formalized as a schema that can be extended or customized, enabling consistent labeling, classification, and communication about different types of harms across teams and systems.

Solves for

establish a shared vocabulary and taxonomy for discussing different types of harms in my organizationextend the base taxonomy with domain-specific harm categories (e.g., medical misinformation, financial fraud)ensure consistent harm classification across multiple safety systems and teamsmap my internal risk taxonomy to WildGuard categories for standardized evaluation

Best for

organizations building safety policies and defining acceptable use guidelines

teams implementing multi-stage safety pipelines with consistent harm definitions

researchers studying harm taxonomy design and safety classification

Requires

Understanding of WildGuard's harm taxonomy and classification schema

Documentation of domain-specific harm categories if extending the base taxonomy

Limitations

Taxonomy is opinionated and may not align with all cultural contexts or use cases

Some harms are context-dependent and difficult to classify into discrete categories (e.g., misinformation)

Taxonomy does not capture severity or impact — only harm type

What makes it unique

WildGuard's taxonomy is empirically-derived from adversarial examples and real-world harmful prompts, covering both obvious harms (violence, illegal) and subtle ones (indirect harm, context-dependent violations). The taxonomy is formalized as an extensible schema enabling customization while maintaining compatibility with pre-trained classifiers.

vs alternatives

More comprehensive and adversarially-informed than generic content moderation taxonomies, and more structured than ad-hoc harm definitions, providing a standardized reference for safety classification across teams and systems.

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

Related Artifactssharing capabilities

Artifacts that share capabilities with WildGuard, ranked by overlap. Discovered automatically through the match graph.

Model20

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specialized harm category detectionmulti-category prompt safety classificationresponse-level content safety classification

3 shared capabilities

Framework43

Rebuff

Self-hardening prompt injection detector with multi-layer defense.

multi-layered heuristic prompt injection detectionllm-based semantic prompt injection detection

2 shared capabilities

Product29

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Secure, manage, and comply GenAI enterprise applications...

prompt-injection-detectionsensitive-data-classification-and-tagging

2 shared capabilities

Product29

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Unlock AI-driven productivity with customized, form-based prompt...

prompt quality scoring and content moderation guardrails

1 shared capability

Model56

Llama-3.1-8B-Instruct

text-generation model by undefined. 94,68,562 downloads.

safety-aligned response generation with refusal capabilities

1 shared capability

Product17

PromptPerfect

Tool for prompt engineering.

prompt security and injection vulnerability detection

1 shared capability

Best For

✓LLM application developers building safety layers for public-facing chatbots
✓teams deploying multi-tenant AI systems requiring per-user risk assessment
✓security researchers evaluating adversarial robustness of AI systems
✓production LLM systems requiring defense-in-depth safety architecture
✓teams conducting red-teaming or adversarial testing of LLM outputs
✓compliance-heavy industries (finance, healthcare, legal) needing output audit trails
✓LLM developers evaluating safety training and alignment techniques
✓teams conducting systematic red-teaming to measure refusal robustness

Known Limitations

⚠Classification accuracy varies by harm category — some edge cases (e.g., implicit threats) require human review
⚠Model trained primarily on English text; cross-lingual performance not fully characterized
⚠Requires inference latency of ~50-200ms per prompt depending on model size and hardware
⚠Cannot distinguish between hypothetical/educational requests and genuine harmful intent without additional context
⚠Response classification is context-dependent — same text may be safe in educational context but harmful in other contexts; model has limited context window
⚠Inference latency adds ~100-300ms per response, impacting real-time streaming use cases

Requirements

Python 3.8+PyTorch 1.9+ or TensorFlow 2.6+Pre-trained WildGuard model weights (available via HuggingFace or GitHub)GPU recommended for production inference (CPU inference ~500ms per prompt)WildGuard response classifier model weightsGPU strongly recommended for production (CPU inference ~1-2s per response)WildGuard refusal detection modelPaired dataset of (harmful prompt, LLM response) for evaluation

Input / Output

Accepts: text (raw user prompts, chat messages), structured JSON (prompt + metadata like user ID, conversation context), text (LLM-generated response, full response or streaming chunks), structured JSON (response + original prompt + conversation history for context), text (harmful prompt + LLM response pair), structured JSON (prompt, response, harm category, attack type metadata), structured JSON/CSV (prompts, responses, labels, metadata), HuggingFace dataset format, raw text files with annotation metadata, list of LLMs to evaluate (model names/endpoints), adversarial prompt set (from WildGuard dataset or custom), evaluation configuration (harm categories, refusal thresholds, etc.), pre-trained WildGuard model weights, labeled training data (text + harm category labels), training configuration (learning rate, batch size, epochs, etc.), base WildGuard taxonomy (JSON schema), custom harm categories and definitions (for extension)

Produces: classification label (harmful/safe), confidence score (0-1), harm category distribution (probabilities across violence, illegal, sexual, hate, self-harm, etc.), structured JSON with category-level scores, harm category distribution, structured JSON with per-category scores and reasoning, refusal classification (appropriate refusal, partial refusal, no refusal, harmful compliance), structured JSON with refusal quality metrics, labeled dataset splits (train/val/test), structured records with prompt, response, harm category, confidence, refusal quality, metadata including attack type, model source, human annotation agreement scores, structured evaluation report (JSON/CSV), per-model safety metrics (refusal rate, harm detection rate, category-specific scores), comparative visualizations (charts, heatmaps), failure mode analysis (which attack types succeed against which models), fine-tuned model weights, training metrics (loss, accuracy, per-category F1 scores), evaluation results on validation/test sets, model artifacts ready for deployment (ONNX, TorchScript, etc.), structured harm taxonomy (JSON schema), category definitions and examples, mapping between custom categories and WildGuard categories

UnfragileRank

Adoption70%(35% weight)

Quality23%(25% weight)

Ecosystem40%(20% weight)

Match Graph10%(15% weight)

Freshness100%(5% weight)

UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.

Type: Dataset

7 capabilities

Visit WildGuard→

About

Allen AI's safety classification dataset and model for detecting harmful prompts and responses, covering prompt harmfulness, response harmfulness, and refusal detection with high accuracy across diverse risk scenarios.

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Capabilities7 decomposed

multi-class prompt harmfulness classification

Medium confidence

Solves for

Best for

LLM application developers building safety layers for public-facing chatbots

teams deploying multi-tenant AI systems requiring per-user risk assessment

security researchers evaluating adversarial robustness of AI systems

Requires

Python 3.8+

PyTorch 1.9+ or TensorFlow 2.6+

Pre-trained WildGuard model weights (available via HuggingFace or GitHub)

Limitations

Classification accuracy varies by harm category — some edge cases (e.g., implicit threats) require human review

Model trained primarily on English text; cross-lingual performance not fully characterized

Requires inference latency of ~50-200ms per prompt depending on model size and hardware

What makes it unique

vs alternatives

response-level harm detection and classification

Medium confidence

Solves for

Best for

production LLM systems requiring defense-in-depth safety architecture

teams conducting red-teaming or adversarial testing of LLM outputs

compliance-heavy industries (finance, healthcare, legal) needing output audit trails

Requires

Python 3.8+

PyTorch 1.9+ or TensorFlow 2.6+

WildGuard response classifier model weights

Limitations

Response classification is context-dependent — same text may be safe in educational context but harmful in other contexts; model has limited context window

Inference latency adds ~100-300ms per response, impacting real-time streaming use cases

False positives on sarcasm, satire, or educational content about harmful topics (e.g., explaining how to recognize scams)

What makes it unique

vs alternatives

refusal detection and compliance scoring

Medium confidence

Solves for

Best for

LLM developers evaluating safety training and alignment techniques

teams conducting systematic red-teaming to measure refusal robustness

researchers studying jailbreak resistance and adversarial robustness

Requires

Python 3.8+

PyTorch 1.9+ or TensorFlow 2.6+

WildGuard refusal detection model

Limitations

Refusal quality is subjective — model may disagree with human judgment on what constitutes 'appropriate' refusal

Requires paired data (harmful prompt + response) for meaningful evaluation; cannot assess refusal in isolation

Does not evaluate whether refusals are helpful or provide constructive alternatives

What makes it unique

vs alternatives

adversarial dataset curation and annotation

Medium confidence

Solves for

Best for

researchers developing new safety techniques and alignment methods

teams building domain-specific safety classifiers (e.g., for medical or financial LLMs)

organizations conducting systematic adversarial testing and red-teaming

Requires

Python 3.8+ for dataset loading and processing

HuggingFace datasets library or direct GitHub access

Sufficient storage for full dataset (~1-5GB depending on format)

Limitations

Dataset is primarily English-language; limited coverage of non-English harm patterns and cultural context

Annotations reflect human judgment which may not align with all use cases or cultural norms

Dataset size is finite (~10k-100k examples depending on category); may not cover all emerging attack vectors

What makes it unique

vs alternatives

multi-model safety evaluation and benchmarking

Medium confidence

Solves for

Best for

teams evaluating LLM providers for production deployment with safety requirements

researchers conducting comparative studies of LLM safety and alignment

organizations tracking safety metrics over time as models are updated

Requires

Python 3.8+

API keys or local access to LLMs being evaluated

WildGuard classifier models

Limitations

Evaluation is only as good as the WildGuard classifier — systematic biases in the classifier will skew comparisons

Requires running inference on all models being compared, which is expensive and time-consuming for large-scale evaluation

Results are specific to the WildGuard dataset and taxonomy; may not generalize to other harm definitions or use cases

What makes it unique

vs alternatives

fine-tuning and custom classifier training

Medium confidence

Solves for

Best for

teams with domain-specific safety requirements (medical, financial, legal, etc.)

organizations with proprietary data that cannot be shared with external safety services

developers building edge-deployed safety systems with latency/size constraints

Requires

Python 3.8+

PyTorch 1.9+ or TensorFlow 2.6+

HuggingFace transformers library

Limitations

Fine-tuning requires labeled training data; quality and quantity directly impact classifier performance

Transfer learning effectiveness depends on similarity between WildGuard training data and target domain

Fine-tuned models may overfit to specific domains and perform poorly on out-of-distribution examples

What makes it unique

vs alternatives

harm category taxonomy and schema definition

Medium confidence

Solves for

Best for

organizations building safety policies and defining acceptable use guidelines

teams implementing multi-stage safety pipelines with consistent harm definitions

researchers studying harm taxonomy design and safety classification

Requires

Understanding of WildGuard's harm taxonomy and classification schema

Documentation of domain-specific harm categories if extending the base taxonomy

Limitations

Taxonomy is opinionated and may not align with all cultural contexts or use cases

Some harms are context-dependent and difficult to classify into discrete categories (e.g., misinformation)

Taxonomy does not capture severity or impact — only harm type

What makes it unique

vs alternatives

Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.

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WildGuard

Capabilities7 decomposed

multi-class prompt harmfulness classification

response-level harm detection and classification

refusal detection and compliance scoring

adversarial dataset curation and annotation

multi-model safety evaluation and benchmarking

fine-tuning and custom classifier training

harm category taxonomy and schema definition

Related Artifactssharing capabilities

Llama Guard 3 8B

Rebuff

Aim Security

PromptInterface.ai

Llama-3.1-8B-Instruct

PromptPerfect

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to WildGuard

Are you the builder of WildGuard?

Get the weekly brief

Data Sources

WildGuard

Capabilities7 decomposed

multi-class prompt harmfulness classification

response-level harm detection and classification

refusal detection and compliance scoring

adversarial dataset curation and annotation

multi-model safety evaluation and benchmarking

fine-tuning and custom classifier training

harm category taxonomy and schema definition

Related Artifactssharing capabilities

Llama Guard 3 8B

Rebuff

Aim Security

PromptInterface.ai

Llama-3.1-8B-Instruct

PromptPerfect

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to WildGuard

Are you the builder of WildGuard?

Get the weekly brief

Data Sources