{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"llama-guard-3","slug":"llama-guard-3","name":"Llama Guard 3","type":"model","url":"https://github.com/meta-llama/PurpleLlama","page_url":"https://unfragile.ai/llama-guard-3","categories":["testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"llama-guard-3__cap_0","uri":"capability://safety.moderation.multi.category.harmful.content.classification.for.llm.inputs.and.outputs","name":"multi-category harmful content classification for llm inputs and outputs","description":"Llama Guard 3 classifies text inputs and outputs against a taxonomy of harmful content categories including violence, sexual content, criminal planning, self-harm, and other risk domains. The model uses a fine-tuned transformer architecture trained on adversarial examples and safety-focused datasets to produce binary or multi-class predictions with confidence scores, enabling deployment as a guardrail layer that can block or flag unsafe content before it reaches users or after generation.","intents":["I need to filter user prompts before they reach my LLM to prevent jailbreak attempts and harmful requests","I want to scan LLM outputs before returning them to users to catch unsafe generations","I need to classify content across multiple risk categories to apply different handling policies per category","I want to measure the safety of my LLM deployment by monitoring what percentage of requests are flagged as harmful"],"best_for":["teams deploying open-source LLMs in production who need safety guardrails","organizations building chatbots or conversational AI that must comply with content policies","researchers evaluating LLM safety and building red-team/blue-team security assessments"],"limitations":["Classification accuracy varies by risk category; some edge cases (sarcasm, context-dependent harm) may be misclassified","Requires tuning confidence thresholds per use case; no one-size-fits-all blocking strategy","Adds inference latency (~50-200ms per classification depending on hardware) to request/response pipeline","Trained primarily on English; multilingual performance not fully documented","Cannot detect novel or emerging harm categories not represented in training data"],"requires":["Python 3.8+","PyTorch 1.13+ or compatible inference framework (vLLM, TensorRT, ONNX Runtime)","Model weights (8B or 1B parameter versions available from Meta)","GPU with 8GB+ VRAM for reasonable inference speed, or CPU for batch processing"],"input_types":["plain text (user prompts, LLM outputs)","structured conversation turns (user message + assistant response pairs)"],"output_types":["binary classification (safe/unsafe)","multi-class category predictions (violence, sexual, criminal, etc.)","confidence scores per category","structured JSON with category breakdown"],"categories":["safety-moderation","content-filtering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-guard-3__cap_1","uri":"capability://safety.moderation.red.team.and.blue.team.cybersecurity.benchmarking.framework.cyberseceval","name":"red-team and blue-team cybersecurity benchmarking framework (cyberseceval)","description":"CyberSecEval is a comprehensive evaluation suite that tests LLMs against cybersecurity attack scenarios including prompt injection, MITRE ATT&CK techniques, code interpreter abuse, vulnerability exploitation, spear phishing, and autonomous offensive cyber operations. The framework abstracts multiple LLM providers (OpenAI, Anthropic, Google, Together) through a unified interface, executes benchmark datasets against target models, and produces structured results measuring both offensive capabilities and defensive robustness.","intents":["I need to evaluate whether my LLM is vulnerable to prompt injection and jailbreak attacks before deploying it","I want to measure my LLM's propensity to generate malicious code or assist with cyberattacks","I need to benchmark multiple LLM providers (OpenAI, Anthropic, Llama) on the same security evaluation to compare their safety profiles","I want to identify false refusal rates where my LLM incorrectly blocks legitimate security research requests"],"best_for":["LLM providers and researchers conducting safety evaluations before model release","security teams assessing third-party LLM APIs for deployment risk","red-teamers and security researchers building adversarial test suites"],"limitations":["Benchmark execution requires API keys for multiple LLM providers, incurring costs for each evaluation run","Results are point-in-time snapshots; LLM behavior changes with model updates and fine-tuning","Some benchmarks (e.g., autonomous cyber operations) may be sensitive and require responsible disclosure","Evaluation coverage is not exhaustive; novel attack vectors may not be represented in current benchmarks","Requires significant compute resources to run full benchmark suite across multiple models"],"requires":["Python 3.9+","API keys for target LLM providers (OpenAI, Anthropic, Google Generative AI, Together AI, or local Llama models)","Network access to LLM APIs or local model serving infrastructure","Benchmark datasets (provided in repo as JSON files)","Compute resources for running evaluations (can be parallelized)"],"input_types":["benchmark datasets (JSON format with attack prompts, expected behaviors)","LLM provider configurations (API endpoints, model names, parameters)","evaluation parameters (batch size, timeout, retry logic)"],"output_types":["structured evaluation results (JSON with pass/fail per benchmark)","aggregated metrics (success rate, false refusal rate, category breakdown)","detailed logs of LLM responses and reasoning"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-guard-3__cap_10","uri":"capability://safety.moderation.prompt.guard.prompt.injection.detection","name":"prompt guard prompt injection detection","description":"Specialized safety model that detects prompt injection attacks in user inputs with high precision, using techniques to identify when user input is attempting to override system instructions or manipulate model behavior. Prompt Guard is designed to be deployed as an input filter before requests reach the main LLM, with low false positive rates to avoid blocking legitimate user queries.","intents":["I need to detect prompt injection attacks in user inputs before they reach my LLM","I want a specialized model for injection detection that's faster and more accurate than general-purpose safety classifiers","I need to filter malicious prompts while minimizing false positives that block legitimate requests"],"best_for":["teams deploying LLMs in high-security contexts where prompt injection is a primary threat","applications with strict false positive requirements (e.g., customer support where blocking legitimate requests is costly)","organizations needing specialized injection detection beyond general content safety"],"limitations":["Specialized for prompt injection; doesn't detect other harm categories (violence, sexual content, etc.)","Requires tuning confidence thresholds per use case; no universal threshold works for all contexts","May miss sophisticated injection techniques not represented in training data","Performance varies by input length and complexity"],"requires":["Python 3.8+","Prompt Guard model weights (from Meta)","PyTorch or compatible inference framework","GPU with 4GB+ VRAM for reasonable inference speed"],"input_types":["user prompts (text)","system prompts (for context)"],"output_types":["binary classification (injection/safe)","confidence score","injection technique identified (if applicable)"],"categories":["safety-moderation","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-guard-3__cap_11","uri":"capability://safety.moderation.codeshield.code.security.analysis.and.vulnerability.detection","name":"codeshield code security analysis and vulnerability detection","description":"Specialized safety model that analyzes code snippets for security vulnerabilities, insecure patterns, and dangerous operations. CodeShield can be deployed as an output filter to scan LLM-generated code before returning it to users, or as an input filter to detect requests for malicious code generation. The model identifies vulnerability types and provides reasoning for security decisions.","intents":["I need to scan LLM-generated code for security vulnerabilities before returning it to users","I want to detect requests for malicious code generation and refuse them","I need to identify specific vulnerability types in code (SQL injection, buffer overflow, etc.)"],"best_for":["teams deploying code generation LLMs (Copilot-like products)","organizations where generated code is executed in production environments","security teams evaluating LLM-assisted development tools"],"limitations":["Specialized for code security; doesn't detect non-code harms","Accuracy varies by programming language; may be weaker for less common languages","Cannot detect all vulnerability types; novel or context-dependent vulnerabilities may be missed","False positives are common (secure code flagged as insecure due to unfamiliar patterns)","Requires understanding of code context; isolated code snippets may be misclassified"],"requires":["Python 3.8+","CodeShield model weights (from Meta)","PyTorch or compatible inference framework","GPU with 4GB+ VRAM for reasonable inference speed"],"input_types":["code snippets (Python, JavaScript, C, Java, etc.)","code generation requests (prompts asking for code)"],"output_types":["security classification (secure/insecure)","vulnerability types identified","confidence score","reasoning/explanation"],"categories":["safety-moderation","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-guard-3__cap_12","uri":"capability://safety.moderation.model.card.and.safety.documentation.generation","name":"model card and safety documentation generation","description":"Meta provides detailed model cards and safety documentation for Llama Guard 3 and other safety models, documenting training data, evaluation results, known limitations, and recommended deployment practices. These artifacts serve as reference documentation for practitioners deploying the models, including guidance on threshold tuning, false refusal rates, and integration patterns.","intents":["I need to understand the training data and evaluation methodology for Llama Guard 3 before deploying it","I want to know the known limitations and failure modes of the safety model","I need guidance on how to tune confidence thresholds for my specific use case"],"best_for":["teams deploying Llama Guard 3 in production who need to understand model capabilities and limitations","security teams conducting due diligence on safety models","researchers studying safety model design and evaluation"],"limitations":["Documentation is static; model behavior may change with updates","Guidance is general; specific tuning for niche use cases requires additional experimentation","Known limitations are disclosed but may not be exhaustive"],"requires":["Access to model card documentation (provided in repo)"],"input_types":["none (documentation artifact)"],"output_types":["structured documentation (markdown, JSON)","evaluation results and metrics","deployment recommendations"],"categories":["safety-moderation","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-guard-3__cap_2","uri":"capability://tool.use.integration.llm.provider.abstraction.layer.with.unified.inference.interface","name":"llm provider abstraction layer with unified inference interface","description":"The core infrastructure provides an abstraction layer that unifies inference calls across multiple LLM providers (OpenAI, Anthropic, Google Generative AI, Together AI, local Llama models) through a common Python interface. This layer handles provider-specific API differences, authentication, request/response formatting, error handling, and caching, allowing benchmark code and safety tools to run against any provider without modification.","intents":["I want to run the same safety evaluation against OpenAI, Anthropic, and local Llama models without rewriting code for each provider","I need to abstract away provider-specific API quirks so my safety tool works with any LLM backend","I want to cache LLM responses to reduce API costs and latency during repeated evaluations"],"best_for":["researchers and teams evaluating multiple LLM providers on the same benchmarks","developers building LLM applications that need to support multiple backends","organizations migrating between LLM providers and needing a compatibility layer"],"limitations":["Abstraction adds ~10-50ms overhead per request due to wrapper logic and serialization","Not all provider-specific features are exposed (e.g., vision capabilities, function calling schemas vary)","Caching is in-memory only; no distributed cache support for multi-machine deployments","Error handling is generic; provider-specific errors may be masked or require custom handling"],"requires":["Python 3.8+","API keys for target providers (OpenAI, Anthropic, Google, Together) OR local Llama model serving","Network connectivity for cloud providers OR local inference server (vLLM, Ollama, etc.)"],"input_types":["unified request objects (prompt text, model name, temperature, max_tokens, etc.)","provider configuration (API endpoint, authentication credentials)"],"output_types":["unified response objects (generated text, stop reason, token counts)","structured error objects with provider-agnostic error codes"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-guard-3__cap_3","uri":"capability://safety.moderation.prompt.injection.and.jailbreak.vulnerability.testing","name":"prompt injection and jailbreak vulnerability testing","description":"Specialized benchmark module that tests LLM susceptibility to prompt injection attacks including instruction override, context confusion, and adversarial prompt techniques. The framework executes a curated dataset of injection prompts against target models, measures success rates (whether the LLM follows the injected instruction instead of the original system prompt), and identifies false refusal rates where legitimate requests are blocked.","intents":["I need to test whether my LLM is vulnerable to prompt injection before deploying it in production","I want to measure the false refusal rate of my safety guardrails to ensure they don't block legitimate requests","I need to understand which injection techniques are most effective against my model so I can prioritize mitigations"],"best_for":["LLM product teams conducting pre-release security testing","security researchers studying prompt injection vulnerabilities","teams deploying LLMs in high-stakes applications (customer support, content moderation)"],"limitations":["Benchmark results are specific to the exact model version and system prompt used; results don't transfer across versions","Some injection techniques may be patched in newer model versions, making benchmarks outdated","Measuring 'success' of injection is subjective and requires manual review for edge cases","Adversarial examples in the benchmark may not represent real-world attack patterns"],"requires":["Python 3.9+","Access to target LLM (API or local deployment)","Prompt injection benchmark dataset (provided in repo)","Manual review capability for evaluating injection success"],"input_types":["original system prompt","user query","injected instruction (adversarial prompt)"],"output_types":["binary success/failure per injection attempt","aggregated injection success rate","false refusal rate metrics"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-guard-3__cap_4","uri":"capability://safety.moderation.code.generation.and.interpreter.security.evaluation","name":"code generation and interpreter security evaluation","description":"Benchmark module that evaluates LLM security in code generation and code interpreter contexts, testing the model's propensity to generate insecure code, assist with memory corruption exploits, and abuse code execution environments. The framework includes datasets for secure/insecure code generation, code interpreter abuse scenarios, and vulnerability exploitation, measuring both the LLM's capability to generate malicious code and its resistance to such requests.","intents":["I need to assess whether my LLM generates secure code or introduces vulnerabilities when asked to write functions","I want to test if my LLM can be tricked into generating code that exploits memory corruption or other low-level vulnerabilities","I need to evaluate the security of code interpreter integrations (e.g., Python REPL) when paired with my LLM"],"best_for":["LLM providers offering code generation or code interpreter features","security teams evaluating LLM-powered development tools (Copilot-like products)","researchers studying LLM capabilities in offensive security contexts"],"limitations":["Secure code evaluation requires domain expertise to judge; automated scoring is imperfect","Benchmark datasets may not cover all vulnerability types or programming languages","Results are specific to the programming language and context in the benchmark","False positives are common (secure code flagged as insecure due to style or unfamiliar patterns)"],"requires":["Python 3.9+","Access to target LLM","Code generation benchmark datasets (provided in repo)","Optional: static analysis tools (SAST) for automated code security scoring"],"input_types":["code generation prompts (requests to write functions, scripts, etc.)","code interpreter abuse scenarios (requests to exploit vulnerabilities)","vulnerability exploitation prompts"],"output_types":["generated code samples","security classification (secure/insecure)","vulnerability type identified","aggregated metrics (% secure code, % refusals)"],"categories":["safety-moderation","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-guard-3__cap_5","uri":"capability://safety.moderation.mitre.att.ck.framework.compliance.and.false.refusal.measurement","name":"mitre att&ck framework compliance and false refusal measurement","description":"Benchmark module that evaluates LLM compliance with the MITRE ATT&CK cybersecurity framework by testing whether the model correctly refuses requests aligned with known attack techniques, while also measuring false refusal rates where legitimate security research or defensive questions are incorrectly blocked. The framework uses MITRE-mapped prompts (including multilingual variants) to assess both the model's safety guardrails and their precision.","intents":["I need to verify that my LLM correctly refuses requests aligned with MITRE ATT&CK attack techniques","I want to measure false refusal rates to ensure my safety guardrails don't block legitimate security research","I need to evaluate my LLM's behavior on multilingual attack prompts to ensure safety across languages"],"best_for":["LLM providers building safety policies aligned with cybersecurity frameworks","security teams evaluating LLM deployment in regulated industries","researchers studying the trade-off between safety and utility in LLMs"],"limitations":["MITRE ATT&CK mapping is subjective; not all prompts fit cleanly into framework categories","False refusal measurement requires manual review to distinguish legitimate from illegitimate requests","Multilingual variants are machine-translated; translation quality may affect evaluation results","Framework is static; new attack techniques emerge faster than MITRE updates"],"requires":["Python 3.9+","Access to target LLM","MITRE ATT&CK benchmark dataset (provided in repo, includes multilingual variants)","Manual review capability for false refusal assessment"],"input_types":["MITRE ATT&CK-mapped prompts (attack technique descriptions, requests for assistance)","legitimate security research prompts (defensive questions, educational requests)"],"output_types":["refusal rate per MITRE technique","false refusal rate (legitimate requests blocked)","true positive rate (actual attacks refused)","structured results by technique category"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-guard-3__cap_6","uri":"capability://safety.moderation.visual.prompt.injection.vulnerability.testing","name":"visual prompt injection vulnerability testing","description":"Benchmark module (CyberSecEval v3+) that evaluates LLM susceptibility to prompt injection attacks embedded in images, including text overlays, steganographic content, and adversarial visual patterns. The framework tests multimodal LLMs against visual injection datasets and measures whether the model follows injected instructions from image content instead of the original system prompt.","intents":["I need to test whether my multimodal LLM is vulnerable to prompt injection attacks hidden in images","I want to evaluate the security of my vision-enabled LLM before deploying it in production","I need to understand how visual injection techniques compare to textual injection in terms of effectiveness"],"best_for":["teams deploying multimodal LLMs (vision + language models)","researchers studying adversarial attacks on vision-language models","security teams evaluating vision-enabled chatbots and assistants"],"limitations":["Requires multimodal LLM support; not applicable to text-only models","Visual injection techniques are rapidly evolving; benchmarks may become outdated quickly","Measuring injection success in multimodal context is more subjective than text-only injection","Benchmark dataset size is smaller than textual injection datasets due to image generation complexity"],"requires":["Python 3.9+","Multimodal LLM with vision capabilities (e.g., GPT-4V, Claude 3 Vision, Llama 3.2 Vision)","Visual prompt injection benchmark dataset (provided in repo)","Image processing libraries (PIL, OpenCV)"],"input_types":["images with embedded text overlays or adversarial patterns","original system prompt","user query"],"output_types":["binary success/failure per visual injection","aggregated visual injection success rate","comparison with textual injection effectiveness"],"categories":["safety-moderation","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-guard-3__cap_7","uri":"capability://safety.moderation.spear.phishing.and.social.engineering.capability.assessment","name":"spear phishing and social engineering capability assessment","description":"Benchmark module (CyberSecEval v3+) that evaluates LLM capability to assist with or generate spear phishing and social engineering attacks. The framework tests whether the model can be prompted to generate convincing phishing emails, impersonation content, or social engineering scripts, measuring both the model's refusal rate and the quality of generated malicious content when refusals are bypassed.","intents":["I need to assess whether my LLM can be abused to generate phishing emails or social engineering content","I want to measure my LLM's resistance to requests for social engineering assistance","I need to evaluate the effectiveness of my safety guardrails against social engineering prompts"],"best_for":["security teams evaluating LLM deployment in organizations vulnerable to phishing","LLM providers assessing misuse risks before release","red-teamers and security researchers studying LLM-assisted social engineering"],"limitations":["Benchmark results may be sensitive; responsible disclosure required before publication","Measuring 'quality' of phishing content is subjective and requires security expertise","Real-world phishing effectiveness depends on context and target; benchmark results may not generalize","Refusal rates don't capture partial compliance (e.g., generating content that's slightly modified but still useful for phishing)"],"requires":["Python 3.9+","Access to target LLM","Spear phishing benchmark dataset (provided in repo)","Security expertise for evaluating generated phishing content"],"input_types":["social engineering prompts (requests to generate phishing emails, impersonation scripts, etc.)","target context (company name, employee role, etc.)"],"output_types":["refusal rate for social engineering requests","quality assessment of generated phishing content","aggregated metrics on social engineering capability"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-guard-3__cap_8","uri":"capability://safety.moderation.autonomous.offensive.cyber.operations.capability.evaluation","name":"autonomous offensive cyber operations capability evaluation","description":"Benchmark module (CyberSecEval v3+) that evaluates LLM capability to function as an autonomous agent in offensive cybersecurity scenarios, including network reconnaissance, vulnerability discovery, exploitation, and lateral movement. The framework tests whether the model can decompose complex attack objectives into sub-tasks, maintain state across multiple interactions, and execute multi-step attack chains.","intents":["I need to assess whether my LLM can be used as an autonomous cyber attack agent","I want to measure my LLM's capability to plan and execute multi-step attack scenarios","I need to evaluate the risk of my LLM being used for autonomous offensive cyber operations"],"best_for":["LLM providers conducting comprehensive security evaluation before release","government and defense organizations assessing LLM security risks","researchers studying LLM capabilities in autonomous attack scenarios"],"limitations":["Benchmark results are highly sensitive; restricted distribution required","Autonomous attack evaluation is complex and subjective; requires significant security expertise","Real-world attack success depends on target environment; benchmark results may not generalize","Benchmark execution may be restricted or prohibited in some jurisdictions","Results can be misused if disclosed; responsible disclosure is critical"],"requires":["Python 3.9+","Access to target LLM","Autonomous cyber operations benchmark dataset (provided in repo, may be restricted)","Significant security expertise and infrastructure for safe evaluation","Approval from organization leadership for sensitive security testing"],"input_types":["high-level attack objectives (e.g., 'gain access to target network')","target environment descriptions (network topology, systems, vulnerabilities)","feedback from simulated environment (success/failure of actions)"],"output_types":["attack plan decomposition (sub-tasks and sequencing)","success rate for multi-step attack scenarios","capability assessment (reconnaissance, exploitation, lateral movement)","aggregated autonomous attack capability metrics"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-guard-3__cap_9","uri":"capability://safety.moderation.llamafirewall.modular.security.scanning.and.filtering","name":"llamafirewall modular security scanning and filtering","description":"LlamaFirewall is a modular security framework that implements multiple scanner components for input/output filtering, including Llama Guard integration, Prompt Guard for injection detection, and CodeShield for code security analysis. The framework allows composition of multiple scanners in a pipeline, with configurable policies per scanner and support for custom scanner implementations, enabling flexible security posture configuration for different deployment contexts.","intents":["I need to deploy multiple security scanners (content safety, prompt injection, code security) in a single pipeline","I want to configure different security policies for different use cases (e.g., stricter for customer-facing, looser for internal tools)","I need to integrate custom security checks alongside Meta's provided scanners"],"best_for":["teams deploying LLMs with complex security requirements across multiple dimensions","organizations needing modular, composable security architecture","developers building custom security scanners that need to integrate with standard frameworks"],"limitations":["Pipeline composition adds latency; each scanner adds ~50-200ms depending on implementation","No built-in distributed execution; all scanners run sequentially on single machine","Policy configuration is manual; no automatic policy optimization or learning","Requires understanding of each scanner's output format and confidence thresholds"],"requires":["Python 3.8+","LlamaFirewall framework (from PurpleLlama repo)","Individual scanner models/implementations (Llama Guard, Prompt Guard, CodeShield)","Configuration files defining scanner pipeline and policies"],"input_types":["text (user prompts, LLM outputs)","code (for CodeShield analysis)","structured scanner configurations"],"output_types":["per-scanner results (classification, confidence, reasoning)","aggregated security decision (allow/block/flag)","structured JSON with all scanner outputs"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"llama-guard-3__headline","uri":"capability://safety.moderation.ai.safety.classifier.for.llms","name":"ai safety classifier for llms","description":"Llama Guard 3 is a safety classifier model designed to detect harmful content in large language model inputs and outputs, serving as a crucial guardrail for responsible AI deployment.","intents":["best AI safety classifier","AI content moderation for LLMs","how to detect harmful content in AI","safeguard for generative AI models","LLM risk assessment tools"],"best_for":["companies deploying LLMs","developers building AI applications"],"limitations":["may not cover all risk categories","requires integration with existing systems"],"requires":["access to LLM outputs","integration capabilities"],"input_types":["text inputs from LLMs"],"output_types":["risk assessment reports","harmful content flags"],"categories":["safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PyTorch 1.13+ or compatible inference framework (vLLM, TensorRT, ONNX Runtime)","Model weights (8B or 1B parameter versions available from Meta)","GPU with 8GB+ VRAM for reasonable inference speed, or CPU for batch processing","Python 3.9+","API keys for target LLM providers (OpenAI, Anthropic, Google Generative AI, Together AI, or local Llama models)","Network access to LLM APIs or local model serving infrastructure","Benchmark datasets (provided in repo as JSON files)","Compute resources for running evaluations (can be parallelized)","Prompt Guard model weights (from Meta)"],"failure_modes":["Classification accuracy varies by risk category; 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