{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"shieldgemma","slug":"shieldgemma","name":"ShieldGemma","type":"model","url":"https://ai.google.dev/gemma/docs/shieldgemma","page_url":"https://unfragile.ai/shieldgemma","categories":["code-review-security","deployment-infra"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"shieldgemma__cap_0","uri":"capability://safety.moderation.sexually.explicit.content.classification","name":"sexually-explicit-content-classification","description":"Classifies input and output text for sexually explicit content using a fine-tuned Gemma language model trained on safety datasets. The model processes natural language through transformer attention mechanisms to detect explicit sexual references, imagery descriptions, and adult content across multiple languages and contexts. Returns confidence scores and categorical severity levels (e.g., safe/unsafe) that can be thresholded for different deployment scenarios.","intents":["Filter user-generated content in chat applications before it reaches other users","Prevent LLM outputs containing explicit sexual material from being returned to end users","Audit conversation logs for policy violations in moderated platforms","Set configurable safety thresholds for different user demographics or regions"],"best_for":["Platform teams building consumer-facing chat or content platforms","LLM application developers needing output filtering without external API calls","Organizations requiring on-device safety classification for privacy compliance"],"limitations":["Classification accuracy varies by language; primarily optimized for English with degraded performance in low-resource languages","Context-dependent false positives possible (e.g., medical/educational discussions of sexuality may be flagged)","Requires GPU for inference at production throughput; CPU inference is significantly slower","No real-time learning from false positives — requires model retraining for adaptation"],"requires":["Gemma model weights (2B or 7B parameter versions)","GPU with sufficient VRAM (minimum 4GB for 2B model, 16GB+ for 7B)","Inference framework: Ollama, vLLM, or Google's Vertex AI","Python 3.8+ or compatible runtime"],"input_types":["plain text","multi-turn conversation transcripts","user messages","LLM-generated completions"],"output_types":["binary classification (safe/unsafe)","confidence score (0.0-1.0)","severity category (e.g., low/medium/high)","structured JSON with reasoning"],"categories":["safety-moderation","content-filtering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"shieldgemma__cap_1","uri":"capability://safety.moderation.dangerous.content.detection","name":"dangerous-content-detection","description":"Identifies and classifies text containing instructions for violence, self-harm, illegal activities, or other dangerous behaviors using semantic understanding of intent and context. The model distinguishes between educational/informational content and actionable dangerous instructions through fine-tuned pattern recognition on safety-labeled datasets. Outputs severity scores and content category tags enabling graduated response policies (e.g., warning vs. blocking).","intents":["Prevent distribution of bomb-making instructions, drug synthesis guides, or self-harm content","Detect and quarantine responses where an LLM accidentally generates dangerous instructions","Flag user messages planning violence or illegal activities for human review","Distinguish between educational content (e.g., history of terrorism) and actionable dangerous content"],"best_for":["Social platforms and forums managing user safety at scale","LLM application teams preventing accidental generation of dangerous content","Crisis intervention platforms needing to identify self-harm risk signals","Content moderation teams requiring automated triage before human review"],"limitations":["Semantic understanding of 'dangerous' is culturally and contextually dependent; may misclassify legitimate self-defense or historical violence discussions","Adversarial prompts designed to evade safety classifiers may bypass detection","No real-time threat assessment (e.g., cannot determine if user has actual capability to execute dangerous instructions)","Requires substantial labeled training data for new dangerous content categories not in original training set"],"requires":["Gemma model weights (2B or 7B versions)","GPU inference capability (4GB+ VRAM minimum)","Integration with inference serving layer (vLLM, Ollama, or Vertex AI)","Python 3.8+ runtime"],"input_types":["plain text","user messages","LLM completions","conversation context"],"output_types":["danger classification (safe/unsafe)","danger category (violence/self-harm/illegal/other)","confidence score (0.0-1.0)","structured JSON with category breakdown"],"categories":["safety-moderation","threat-detection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"shieldgemma__cap_2","uri":"capability://safety.moderation.harassment.and.bullying.detection","name":"harassment-and-bullying-detection","description":"Detects targeted harassment, bullying, and abusive language directed at individuals or groups using contextual language understanding. The model identifies patterns of repeated negative targeting, personal attacks, and coordinated abuse through transformer-based semantic analysis of conversation context and user interaction history. Outputs harassment severity scores and target identification enabling context-aware moderation policies.","intents":["Identify and remove bullying comments targeting specific users in social platforms","Detect coordinated harassment campaigns across multiple messages or users","Flag abusive language in customer support interactions for agent protection","Distinguish between harsh criticism and targeted personal harassment"],"best_for":["Social media platforms and community forums managing user safety","Online gaming platforms protecting players from in-game harassment","Customer support platforms protecting agents from abusive customers","Educational platforms preventing cyberbullying among students"],"limitations":["Harassment detection is highly context-dependent; sarcasm, in-group banter, and reclaimed slurs may be misclassified","Requires conversation history for accurate detection; single-message classification has higher false positive rates","Cultural and linguistic variations in what constitutes harassment are not fully captured","Cannot assess real-world impact or escalation risk of harassment without additional signals (e.g., user vulnerability indicators)"],"requires":["Gemma model weights (2B or 7B)","GPU inference (4GB+ VRAM)","Inference framework (vLLM, Ollama, Vertex AI)","Conversation context storage and retrieval system","Python 3.8+"],"input_types":["single message text","conversation thread","user interaction history","metadata (sender, recipient, timestamp)"],"output_types":["harassment classification (safe/unsafe)","harassment type (personal attack/bullying/coordinated abuse/other)","confidence score (0.0-1.0)","target identification (if applicable)","structured JSON with context analysis"],"categories":["safety-moderation","social-safety"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"shieldgemma__cap_3","uri":"capability://safety.moderation.hate.speech.and.discrimination.detection","name":"hate-speech-and-discrimination-detection","description":"Classifies text containing hate speech, discriminatory language, and slurs targeting protected characteristics (race, ethnicity, religion, gender, sexual orientation, disability, etc.) using fine-tuned semantic understanding. The model recognizes both explicit slurs and coded language/dog whistles through pattern matching on safety-labeled datasets. Outputs hate speech severity, target group identification, and language category enabling nuanced moderation policies.","intents":["Remove hate speech and slurs from user-generated content platforms","Prevent LLM outputs containing discriminatory language or stereotypes","Identify and escalate coordinated hate speech campaigns for investigation","Distinguish between reclaimed language, academic discussion, and hateful intent"],"best_for":["Platforms with diverse user bases requiring inclusive content policies","LLM application teams preventing generation of discriminatory outputs","Content moderation teams managing hate speech at scale","Organizations subject to hate speech regulations (EU, UK, etc.)"],"limitations":["Reclaimed slurs and in-group language may be misclassified as hate speech","Coded language and dog whistles evolve rapidly; model requires frequent retraining to keep pace","Cross-cultural and cross-linguistic hate speech patterns not uniformly covered","Context-dependent: academic discussion of hate speech or historical atrocities may be flagged","No understanding of intent vs. accidental offense; cannot distinguish malicious hate from ignorance"],"requires":["Gemma model weights (2B or 7B)","GPU inference (4GB+ VRAM minimum)","Inference serving (vLLM, Ollama, Vertex AI)","Python 3.8+","Optional: conversation context for intent analysis"],"input_types":["plain text","user messages","LLM completions","conversation context (optional)"],"output_types":["hate speech classification (safe/unsafe)","hate speech category (slur/discriminatory language/stereotype/dog whistle/other)","target group identification (race/ethnicity/religion/gender/sexual orientation/disability/other)","confidence score (0.0-1.0)","structured JSON with detailed categorization"],"categories":["safety-moderation","hate-speech-detection"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"shieldgemma__cap_4","uri":"capability://safety.moderation.configurable.safety.threshold.management","name":"configurable-safety-threshold-management","description":"Enables fine-grained control over safety classification thresholds and policies through configuration parameters applied at inference time. Allows operators to adjust confidence score cutoffs per safety category (e.g., strict filtering for explicit content, lenient for dangerous content), define custom response policies (block/warn/log), and apply different thresholds to different user segments or content types. Implemented through post-processing of model confidence scores against configurable policy rules.","intents":["Deploy stricter safety filtering for child-directed content while allowing more permissive filtering for adult users","Adjust safety thresholds based on regional regulations (e.g., stricter hate speech detection in EU)","Implement graduated responses (warning vs. blocking) based on violation severity","A/B test different safety policies to optimize for user experience vs. safety tradeoff"],"best_for":["Platform teams managing safety policies across multiple user segments or regions","LLM application developers tuning safety/usability tradeoffs for specific use cases","Organizations with evolving safety requirements needing rapid policy iteration","Teams operating under different regulatory regimes requiring policy variation"],"limitations":["Threshold adjustment is post-hoc; does not retrain the model, so cannot improve accuracy for edge cases","Lowering thresholds increases false positives; raising thresholds increases false negatives — no way to improve both simultaneously","Policy configuration complexity grows with number of categories and segments; requires careful testing to avoid unintended interactions","No built-in A/B testing framework; requires external experimentation infrastructure"],"requires":["ShieldGemma model deployed with inference serving","Configuration management system (JSON/YAML files or configuration API)","Policy evaluation logic (can be implemented in application code)","Monitoring/logging to track policy impact"],"input_types":["confidence scores from safety classifiers","policy configuration (JSON/YAML)","user/content metadata (segment, region, content type)"],"output_types":["policy decision (block/warn/allow)","action metadata (reason, severity, recommended response)","structured JSON with policy application details"],"categories":["safety-moderation","configuration-management"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"shieldgemma__cap_5","uri":"capability://safety.moderation.multi.language.safety.classification","name":"multi-language-safety-classification","description":"Applies safety classification across multiple languages using Gemma's multilingual capabilities, enabling consistent content moderation policies across global platforms. The model processes text in 40+ languages through shared transformer embeddings trained on multilingual safety datasets. Outputs language-agnostic safety classifications with per-language confidence adjustments reflecting training data coverage.","intents":["Moderate user-generated content in non-English languages on global platforms","Prevent LLM outputs containing unsafe content in any supported language","Maintain consistent safety policies across regions with different primary languages","Detect code-switching (mixing languages) and multilingual harassment patterns"],"best_for":["Global platforms with diverse user bases across 10+ languages","Multilingual LLM applications requiring consistent safety filtering","Organizations expanding to new markets requiring rapid safety policy deployment","Platforms serving immigrant communities with code-switching patterns"],"limitations":["Performance varies significantly by language; high-resource languages (English, Spanish, Mandarin) have higher accuracy than low-resource languages","Cultural context for safety varies by language/region; single model may not capture region-specific norms","Slurs and coded language are language-specific; model may miss language-specific hate speech","Code-switching detection is limited; model may misclassify mixed-language content","No language identification required but implicit; may misclassify language-ambiguous content"],"requires":["Gemma multilingual model weights (2B or 7B)","GPU inference (4GB+ VRAM)","Inference framework supporting multilingual models","Python 3.8+","Optional: language identification system for logging/analysis"],"input_types":["text in 40+ supported languages","code-switched text (multiple languages mixed)","transliterated text (e.g., Hinglish)"],"output_types":["safety classification (safe/unsafe)","safety category (explicit/dangerous/harassment/hate speech)","confidence score (0.0-1.0)","per-language confidence adjustment","detected language (optional)"],"categories":["safety-moderation","multilingual-processing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"shieldgemma__cap_6","uri":"capability://safety.moderation.batch.content.classification.with.scoring","name":"batch-content-classification-with-scoring","description":"Processes multiple text inputs (messages, comments, completions) in batch mode with vectorized inference, returning safety scores and classifications for all inputs simultaneously. Implemented through batching at the inference layer to maximize GPU utilization and throughput. Outputs structured results with per-input classifications, confidence scores, and category breakdowns enabling efficient content moderation pipelines.","intents":["Moderate thousands of user comments or messages daily without per-request latency","Audit historical content in bulk to identify policy violations","Pre-filter LLM outputs in batch generation scenarios (e.g., content recommendation systems)","Generate safety metrics and reports across large content corpora"],"best_for":["Content moderation teams processing high-volume user-generated content","Batch content audit and compliance workflows","LLM applications generating multiple outputs requiring safety filtering","Analytics teams computing safety metrics across content corpora"],"limitations":["Batch processing introduces latency; not suitable for real-time moderation of single messages","Batch size is constrained by GPU VRAM; very large batches require splitting across multiple inference calls","No streaming output; must wait for entire batch to complete before results available","Batch processing efficiency depends on input length uniformity; highly variable input lengths reduce GPU utilization"],"requires":["Gemma model deployed with batch inference support (vLLM, Vertex AI, or custom batching layer)","GPU with sufficient VRAM for batch size (8GB+ recommended)","Python 3.8+","Input data in structured format (JSON, CSV, or database)"],"input_types":["list of text strings","structured data with text fields (JSON, CSV)","database query results"],"output_types":["list of safety classifications (per input)","list of confidence scores (per input)","structured JSON with per-input results","aggregated statistics (e.g., % unsafe content)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"shieldgemma__cap_7","uri":"capability://safety.moderation.input.output.filtering.pipeline","name":"input-output-filtering-pipeline","description":"Integrates safety classification into LLM application workflows by filtering both user inputs (before reaching the model) and model outputs (before returning to user). Implemented as middleware in the inference pipeline that applies safety classifiers sequentially or in parallel, with configurable blocking/warning policies. Enables end-to-end safety without modifying the base LLM.","intents":["Prevent LLM from processing harmful user inputs (e.g., jailbreak attempts, abuse)","Block unsafe LLM outputs from reaching users (e.g., explicit content, dangerous instructions)","Implement graduated safety responses (warn user, log violation, block completely)","Maintain audit trail of safety decisions for compliance and debugging"],"best_for":["LLM application developers building consumer-facing chat or content generation products","Teams deploying open-source LLMs (Gemma, Llama, Mistral) requiring safety guardrails","Organizations needing safety filtering without modifying base model","Applications requiring compliance with content safety regulations"],"limitations":["Adds latency to inference pipeline (~100-500ms per classification depending on model size and hardware)","Input filtering may reject legitimate user queries (false positives), degrading user experience","Output filtering may block valid model outputs, requiring careful threshold tuning","No feedback loop; cannot improve classifier accuracy based on user corrections","Requires separate safety model inference; cannot be merged into base LLM for efficiency"],"requires":["ShieldGemma model deployed alongside base LLM","LLM inference framework with middleware/hook support (e.g., LangChain, LlamaIndex, vLLM)","GPU with sufficient VRAM for both base LLM and safety classifier","Python 3.8+","Logging/monitoring infrastructure for safety decisions"],"input_types":["user messages/prompts","LLM completions/outputs","conversation context (optional)"],"output_types":["filtered user input (safe/blocked)","filtered LLM output (safe/blocked)","safety decision metadata (reason, severity, confidence)","audit log entries"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"shieldgemma__cap_8","uri":"capability://safety.moderation.safety.metric.generation.and.reporting","name":"safety-metric-generation-and-reporting","description":"Generates quantitative safety metrics and reports from classification results, enabling monitoring of content safety trends and policy effectiveness. Computes aggregate statistics (% unsafe content by category, false positive rates, policy violation trends) and generates visualizations/dashboards. Implemented through post-processing of classification results with aggregation and statistical analysis.","intents":["Monitor safety metrics over time to detect emerging safety issues or policy drift","Measure false positive/negative rates to optimize safety thresholds","Generate compliance reports for regulators or internal audits","Identify safety hotspots (e.g., specific user segments, content types, or regions with high violation rates)"],"best_for":["Content moderation teams managing safety at scale","Product teams optimizing safety/usability tradeoffs","Compliance and legal teams generating regulatory reports","Data science teams analyzing safety classifier performance"],"limitations":["Metrics are only as accurate as underlying classifier; systematic biases in classifier propagate to metrics","Aggregate metrics can mask important subgroup differences (e.g., high false positive rate for specific languages)","No built-in statistical significance testing; requires external analysis to distinguish signal from noise","Requires substantial historical data for trend analysis; early-stage deployments have limited statistical power"],"requires":["Classification results from ShieldGemma (structured JSON or database)","Analytics/BI tool (Python pandas, SQL, Tableau, Looker, etc.)","Data storage for historical results (database or data warehouse)","Optional: visualization framework (Matplotlib, Plotly, etc.)"],"input_types":["classification results (JSON, CSV, database records)","metadata (timestamp, user segment, content type, region)","ground truth labels (for accuracy measurement)"],"output_types":["aggregate statistics (% unsafe, % by category, false positive/negative rates)","trend analysis (safety metrics over time)","segmented analysis (metrics by user segment, region, content type)","visualizations (charts, dashboards)","compliance reports (PDF, Excel)"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"shieldgemma__headline","uri":"capability://safety.moderation.safety.content.classifier.suite","name":"safety content classifier suite","description":"Google's ShieldGemma is a comprehensive suite of safety content classifiers designed to filter sexually explicit content, dangerous content, harassment, and hate speech, making it ideal for developers seeking robust content moderation solutions.","intents":["best safety content classifier","safety content filtering for applications","top tools for content moderation","AI models for hate speech detection","content filtering solutions for generative AI"],"best_for":["content moderation","AI applications requiring safety filters"],"limitations":[],"requires":[],"input_types":["text","images"],"output_types":[],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Gemma model weights (2B or 7B parameter versions)","GPU with sufficient VRAM (minimum 4GB for 2B model, 16GB+ for 7B)","Inference framework: Ollama, vLLM, or Google's Vertex AI","Python 3.8+ or compatible runtime","Gemma model weights (2B or 7B versions)","GPU inference capability (4GB+ VRAM minimum)","Integration with inference serving layer (vLLM, Ollama, or Vertex AI)","Python 3.8+ runtime","Gemma model weights (2B or 7B)","GPU inference (4GB+ VRAM)"],"failure_modes":["Classification accuracy varies by language; 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