{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"openrouter-meta-llama-llama-guard-3-8b","slug":"meta-llama-llama-guard-3-8b","name":"Llama Guard 3 8B","type":"model","url":"https://openrouter.ai/models/meta-llama~llama-guard-3-8b","page_url":"https://unfragile.ai/meta-llama-llama-guard-3-8b","categories":["testing-quality"],"tags":["meta-llama","api-access","text"],"pricing":{"model":"paid","free":false,"starting_price":"$4.80e-7 per prompt token"},"status":"active","verified":false},"capabilities":[{"id":"openrouter-meta-llama-llama-guard-3-8b__cap_0","uri":"capability://safety.moderation.multi.category.prompt.safety.classification","name":"multi-category prompt safety classification","description":"Classifies incoming user prompts against a taxonomy of 6 content safety categories (violence, illegal activity, self-harm, sexual content, harassment, and specialized harms) using a fine-tuned Llama 3.1 8B backbone. The model outputs structured safety labels with confidence scores, enabling real-time filtering of unsafe requests before they reach downstream LLMs. Uses instruction-following patterns from Llama 3.1 training combined with safety-specific fine-tuning to distinguish between discussing harmful topics (safe) and requesting harmful actions (unsafe).","intents":["I need to block malicious prompts before they reach my LLM API to prevent jailbreaks and abuse","I want to classify user inputs into safety categories to log and monitor attack patterns","I need a lightweight safety gate that runs locally or on-device without external API calls","I want to understand which safety categories my application is most vulnerable to"],"best_for":["LLM application builders implementing safety guardrails","teams deploying multi-tenant LLM services requiring input validation","developers building content moderation pipelines with safety-first architecture"],"limitations":["Classification is binary per category (safe/unsafe) without nuanced severity gradients","May have false positives on legitimate discussions of sensitive topics (e.g., educational content about violence)","8B model size requires ~16GB VRAM for local deployment; smaller quantized versions may degrade accuracy","Trained on English-centric safety data; performance on non-English prompts is undocumented","Does not classify outputs/responses — only input prompts; requires separate model for response safety"],"requires":["API access via OpenRouter or compatible inference endpoint","Input text must be under typical context length (likely ~4K-8K tokens based on Llama 3.1 base)","For local deployment: Python 3.8+, transformers library, CUDA 11.8+ or compatible GPU","For API usage: valid OpenRouter API key"],"input_types":["text (user prompts, chat messages, free-form queries)"],"output_types":["structured JSON with safety category labels and confidence scores","categorical classification (safe/unsafe per category)"],"categories":["safety-moderation","content-filtering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-guard-3-8b__cap_1","uri":"capability://safety.moderation.response.level.content.safety.classification","name":"response-level content safety classification","description":"Classifies LLM-generated outputs (responses, completions, assistant messages) against the same 6-category safety taxonomy to detect when downstream models produce unsafe content. Operates on the same fine-tuned Llama 3.1 8B architecture but is applied post-generation to catch safety failures in model outputs. Enables real-time detection of jailbreak successes, hallucinated harmful instructions, or unintended unsafe content generation.","intents":["I need to filter LLM responses before returning them to users to prevent serving harmful content","I want to detect when my LLM has been successfully jailbroken and log the failure for analysis","I need to implement a safety layer that catches both prompt injection and response generation failures","I want to measure how often my LLM generates unsafe content to track safety improvements"],"best_for":["LLM application builders implementing output filtering","teams running safety audits and red-teaming campaigns","developers building production LLM services with safety SLAs"],"limitations":["Response classification may be less accurate than prompt classification due to longer, more varied output formats","Cannot distinguish between intentional (user-requested) and unintentional unsafe content in responses","Adds latency to response generation pipeline (inference time for 8B model ~100-500ms depending on response length)","Does not provide remediation suggestions — only flags unsafe content; requires separate system to regenerate or sanitize"],"requires":["API access via OpenRouter or compatible inference endpoint","Response text must be under context length limit","For local deployment: Python 3.8+, transformers library, CUDA 11.8+ or compatible GPU"],"input_types":["text (LLM responses, completions, assistant messages)"],"output_types":["structured JSON with safety category labels and confidence scores","categorical classification (safe/unsafe per category)"],"categories":["safety-moderation","content-filtering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-guard-3-8b__cap_2","uri":"capability://safety.moderation.structured.safety.category.scoring.with.confidence.metrics","name":"structured safety category scoring with confidence metrics","description":"Returns safety classifications as structured JSON with per-category confidence scores (typically 0.0-1.0 range) rather than binary pass/fail verdicts, enabling fine-grained safety policy decisions. The model outputs logits or probability distributions across the 6 safety categories, allowing applications to set custom thresholds per category (e.g., stricter on violence, more lenient on political content). Implements a multi-label classification approach where content can be flagged in multiple categories simultaneously.","intents":["I want to set different safety thresholds for different categories based on my application's risk tolerance","I need confidence scores to distinguish between borderline and clearly unsafe content for logging and analysis","I want to implement tiered responses (warn user, require confirmation, block) based on safety confidence","I need to track safety metrics and measure false positive rates per category"],"best_for":["teams implementing nuanced safety policies with category-specific thresholds","developers building safety dashboards and monitoring systems","organizations conducting safety audits and measuring classifier performance"],"limitations":["Confidence scores are model-dependent and may not be well-calibrated across all categories or domains","No built-in explanation for why content was flagged in a specific category — scores alone don't provide interpretability","Threshold tuning requires labeled validation data; optimal thresholds vary by use case and domain","Multi-label approach can produce overlapping or contradictory category flags requiring application-level resolution logic"],"requires":["API access via OpenRouter or compatible inference endpoint that returns full model outputs","Application code to parse and interpret confidence scores and implement threshold logic","Optional: labeled validation dataset to calibrate thresholds for your specific domain"],"input_types":["text (user prompts or LLM responses)"],"output_types":["structured JSON with per-category confidence scores","multi-label classification output"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-guard-3-8b__cap_3","uri":"capability://safety.moderation.specialized.harm.category.detection","name":"specialized harm category detection","description":"Classifies content against specialized harm categories beyond standard content policy violations, including CSAM-related content, illegal activities, self-harm, and harassment. The fine-tuning incorporates patterns for detecting nuanced harms (e.g., grooming language, suicide encouragement) that may not be caught by keyword-based or simple pattern-matching approaches. Uses instruction-following capabilities of Llama 3.1 to understand context and intent rather than relying on surface-level text matching.","intents":["I need to detect CSAM-related content and illegal activity to comply with legal requirements and platform policies","I want to identify self-harm and suicide-related content to trigger crisis intervention workflows","I need to detect harassment and targeted abuse patterns in user interactions","I want to catch sophisticated jailbreak attempts that use indirect language or metaphors to request harmful content"],"best_for":["platforms with legal compliance requirements (CSAM detection, illegal content)","mental health and crisis support applications","community platforms implementing harassment and abuse detection","organizations subject to regulatory oversight (financial services, healthcare)"],"limitations":["Specialized harm detection may have lower precision on edge cases or novel attack patterns not seen during training","Context-dependent harms (e.g., sarcasm, roleplay scenarios) may produce false positives or false negatives","Model cannot verify factual claims (e.g., whether an illegal activity is actually being planned vs. discussed hypothetically)","Specialized categories may have imbalanced training data, leading to category-specific accuracy variations","Does not integrate with external databases or real-time threat intelligence; relies solely on learned patterns"],"requires":["API access via OpenRouter or compatible inference endpoint","Understanding of your jurisdiction's legal requirements for content moderation","For CSAM detection: integration with external reporting systems (NCMEC, IWF) if required by law","Optional: labeled examples of specialized harms in your domain for threshold calibration"],"input_types":["text (user prompts, messages, content)"],"output_types":["structured JSON with specialized harm category flags","confidence scores per specialized category"],"categories":["safety-moderation","compliance"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-guard-3-8b__cap_4","uri":"capability://safety.moderation.batch.safety.classification.with.api.integration","name":"batch safety classification with api integration","description":"Supports batch processing of multiple prompts or responses through OpenRouter's API, enabling efficient classification of large volumes of content without per-request overhead. Integrates with OpenRouter's batch API infrastructure to queue, process, and retrieve safety classifications asynchronously, reducing per-request latency and cost for high-volume moderation pipelines. Handles rate limiting, retries, and result aggregation transparently.","intents":["I need to classify thousands of user messages in my chat logs for safety audits without overwhelming my API quota","I want to implement cost-effective safety classification for high-volume content moderation pipelines","I need to process historical data or bulk content classification without real-time latency constraints","I want to integrate safety classification into my data pipeline without blocking on per-request inference"],"best_for":["teams running safety audits on historical data","high-volume content moderation platforms","developers building batch processing pipelines","organizations optimizing cost per classification"],"limitations":["Batch processing introduces latency (typically hours to days depending on queue depth) — unsuitable for real-time safety gates","Requires managing batch job IDs and polling for results; adds complexity vs. synchronous API calls","Batch API pricing and rate limits may differ from synchronous API; requires separate quota management","Error handling in batch mode is more complex (partial failures, retries, result reconciliation)","No streaming or incremental results — must wait for entire batch to complete"],"requires":["OpenRouter API key with batch API access enabled","Application code to manage batch job submission, polling, and result retrieval","Ability to handle asynchronous workflows and job state management","Python 3.8+ or equivalent for batch processing client"],"input_types":["text (multiple prompts or responses in batch format)"],"output_types":["structured JSON with safety classifications for each input","batch job metadata and status information"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-guard-3-8b__cap_5","uri":"capability://safety.moderation.multi.language.safety.classification.with.english.primary.accuracy","name":"multi-language safety classification with english-primary accuracy","description":"Classifies safety across multiple languages using the same fine-tuned Llama 3.1 8B model, leveraging the base model's multilingual capabilities. However, safety fine-tuning is primarily optimized for English, with varying accuracy across other languages depending on training data representation. The model uses cross-lingual transfer learning to extend English safety patterns to other languages, but performance degrades gracefully for low-resource languages or non-Latin scripts.","intents":["I need to moderate user content in multiple languages without deploying separate safety models per language","I want to understand which languages have reliable safety classification and which need additional validation","I need to implement global content moderation with a single model across diverse user bases","I want to detect safety issues in code-mixed or multilingual content"],"best_for":["global platforms with multilingual user bases","teams implementing cost-effective multi-language moderation","developers building international LLM applications"],"limitations":["Safety classification accuracy is significantly lower for non-English languages due to English-centric fine-tuning","Performance on low-resource languages (e.g., Amharic, Tagalog) is undocumented and likely unreliable","Code-mixed content (e.g., Hinglish, Spanglish) may produce inconsistent classifications","Specialized harm detection (CSAM, illegal activity) may be less effective in non-English languages","No language detection or routing — requires application to pre-identify language and validate accuracy per language"],"requires":["API access via OpenRouter","Application code to validate accuracy per language and implement language-specific thresholds","Optional: labeled validation data in target languages to measure actual performance","Understanding of language-specific safety concerns and cultural context"],"input_types":["text in multiple languages (English, Spanish, French, German, Chinese, Japanese, etc.)"],"output_types":["structured JSON with safety classifications","per-language confidence scores (if available)"],"categories":["safety-moderation","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-guard-3-8b__cap_6","uri":"capability://safety.moderation.integration.with.llm.application.frameworks.and.safety.middleware","name":"integration with llm application frameworks and safety middleware","description":"Integrates with LLM frameworks (LangChain, LlamaIndex, Anthropic SDK, OpenAI SDK) and safety middleware systems through standardized API interfaces. Can be deployed as a prompt guard (pre-LLM) or response filter (post-LLM) in application chains, with built-in support for async/await patterns, error handling, and fallback logic. Supports integration with observability platforms for logging, monitoring, and alerting on safety violations.","intents":["I want to add safety classification to my existing LangChain or LlamaIndex application without rewriting code","I need to implement safety as a middleware layer in my LLM application pipeline","I want to log and monitor safety violations with structured telemetry and alerting","I need to integrate safety classification with my existing observability and incident response systems"],"best_for":["developers using LangChain, LlamaIndex, or similar LLM frameworks","teams implementing safety as a cross-cutting concern in LLM applications","organizations with existing observability and incident response infrastructure"],"limitations":["Integration patterns vary by framework; requires framework-specific adapter code","Adds latency to LLM application pipeline (100-500ms per classification depending on input length)","Error handling and fallback logic must be implemented by application (e.g., what to do if safety API is down)","Observability integration requires custom code to map safety classifications to monitoring systems","No built-in caching of classifications — each unique input requires a new API call"],"requires":["LLM framework (LangChain, LlamaIndex, Anthropic SDK, OpenAI SDK, etc.)","OpenRouter API key","Application code to implement framework-specific integration","Optional: observability platform (Datadog, New Relic, custom logging) for monitoring"],"input_types":["text (prompts or responses from LLM application)"],"output_types":["structured JSON with safety classifications","integration with framework-specific callback/hook systems"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"openrouter-meta-llama-llama-guard-3-8b__cap_7","uri":"capability://safety.moderation.safety.classification.with.custom.policy.enforcement.and.rule.composition","name":"safety classification with custom policy enforcement and rule composition","description":"Provides safety classifications that can be composed with custom policy rules and business logic to implement application-specific safety policies. The model outputs structured category scores that applications can combine with custom rules (e.g., 'block if violence_score > 0.7 AND user_is_minor', 'warn if harassment_score > 0.5 AND user_is_verified'). Enables policy-as-code approaches where safety decisions are driven by composable rules rather than hard-coded thresholds.","intents":["I need to implement different safety policies for different user segments (minors, verified users, enterprise customers)","I want to combine safety classification with business logic (user reputation, account age, content type) to make nuanced decisions","I need to update safety policies without retraining models or changing application code","I want to implement A/B testing of different safety policies and measure their impact"],"best_for":["platforms with complex, multi-tenant safety requirements","teams implementing policy-as-code approaches","organizations needing to adapt safety policies to different jurisdictions or user segments","developers building configurable safety systems"],"limitations":["Rule composition logic must be implemented by application; no built-in policy engine","Complex policy rules can become difficult to maintain and debug as they grow","No built-in conflict resolution when rules produce contradictory decisions","Policy changes require application deployment or configuration updates; not dynamic at runtime without additional infrastructure","Requires careful testing to ensure policy rules don't create unintended safety gaps or false positives"],"requires":["OpenRouter API key","Application code to implement policy rule engine","Configuration management system for policy rules (optional but recommended)","Testing framework to validate policy behavior"],"input_types":["text (prompts or responses)","structured context (user metadata, content type, etc.)"],"output_types":["structured JSON with safety classifications","policy decision output (allow/warn/block) based on rule composition"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":24,"verified":false,"data_access_risk":"low","permissions":["API access via OpenRouter or compatible inference endpoint","Input text must be under typical context length (likely ~4K-8K tokens based on Llama 3.1 base)","For local deployment: Python 3.8+, transformers library, CUDA 11.8+ or compatible GPU","For API usage: valid OpenRouter API key","Response text must be under context length limit","API access via OpenRouter or compatible inference endpoint that returns full model outputs","Application code to parse and interpret confidence scores and implement threshold logic","Optional: labeled validation dataset to calibrate thresholds for your specific domain","Understanding of your jurisdiction's legal requirements for content moderation","For CSAM detection: integration with external reporting systems (NCMEC, IWF) if required by law"],"failure_modes":["Classification is binary per category (safe/unsafe) without nuanced severity gradients","May have false positives on legitimate discussions of sensitive topics (e.g., educational content about violence)","8B model size requires ~16GB VRAM for local deployment; smaller quantized versions may degrade accuracy","Trained on English-centric safety data; performance on non-English prompts is undocumented","Does not classify outputs/responses — only input prompts; requires separate model for response safety","Response classification may be less accurate than prompt classification due to longer, more varied output formats","Cannot distinguish between intentional (user-requested) and unintentional unsafe content in responses","Adds latency to response generation pipeline (inference time for 8B model ~100-500ms depending on response length)","Does not provide remediation suggestions — only flags unsafe content; requires separate system to regenerate or sanitize","Confidence scores are model-dependent and may not be well-calibrated across all categories or domains","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"ecosystem":0.24,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.35,"quality":0.2,"ecosystem":0.1,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:24.484Z","last_scraped_at":"2026-05-03T15:20:45.776Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=meta-llama-llama-guard-3-8b","compare_url":"https://unfragile.ai/compare?artifact=meta-llama-llama-guard-3-8b"}},"signature":"/43IgYcksrH5/EacRt80wX1/U5QdS3JDOXbrkvkFXcKDss8LhyWKQwbeKHt5oOunevpsFL372iY21TcIENp2BA==","signedAt":"2026-06-21T08:54:57.755Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/meta-llama-llama-guard-3-8b","artifact":"https://unfragile.ai/meta-llama-llama-guard-3-8b","verify":"https://unfragile.ai/api/v1/verify?slug=meta-llama-llama-guard-3-8b","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}