What can Llama Guard 3 do?

multi-category harmful content classification for llm inputs and outputs, adversarial prompt injection vulnerability detection, multi-provider llm abstraction layer for benchmark orchestration, caching and batch processing for benchmark evaluation efficiency, quantized model deployment for resource-constrained environments, llamafirewall integration for real-time scanning pipelines, cybersecurity benchmark evaluation framework (cyberseceval), prompt injection vulnerability testing with visual and textual attack vectors, code interpreter abuse and secure code generation evaluation, autonomous offensive cyber operations capability assessment, spear phishing and social engineering effectiveness evaluation, mitre att&ck framework compliance and false refusal rate measurement

Llama Guard 3

ModelFree

Meta's safety classifier for LLM content moderation.

Open Source

/ 100

12 capabilities

Capabilities12 decomposed

multi-category harmful content classification for llm inputs and outputs

Medium confidence

Llama Guard 3 classifies text inputs and outputs across 13+ risk categories (violence, sexual content, criminal planning, etc.) using a fine-tuned transformer-based safety classifier. The model operates as a standalone inference layer that can be deployed upstream (pre-generation) or downstream (post-generation) in LLM pipelines, returning structured risk assessments with category-level confidence scores rather than binary safe/unsafe verdicts.

Solves for

I need to filter harmful user prompts before they reach my production LLM to prevent jailbreak attemptsI want to catch unsafe model outputs before they're shown to users without blocking all edge casesI need to audit and categorize safety violations in my LLM logs to understand failure patternsI'm building a multi-modal application and need consistent safety classification across text and code

Best for

Teams deploying open-source LLMs (Llama, Mistral, etc.) who need production-grade safety without proprietary APIs

Organizations with strict data residency requirements who cannot use cloud-based safety services

Developers building specialized LLM applications (code generation, medical advice) requiring domain-specific safety tuning

Requires

Python 3.8+

PyTorch 2.0+ or compatible inference runtime (ONNX, vLLM, TensorRT)

4GB+ VRAM for full model inference; 2GB with quantization (int8/int4)

Limitations

Classification latency adds ~50-200ms per request depending on hardware; not suitable for sub-100ms SLA requirements

Trained primarily on English text; multilingual performance degrades significantly for non-English inputs

Risk categories are fixed to Meta's taxonomy; custom category detection requires fine-tuning on proprietary data

What makes it unique

Unlike binary classifiers (OpenAI Moderation API), Llama Guard 3 provides granular multi-category risk assessment with confidence scores, enabling nuanced policy enforcement. Deployed as a local model rather than API, eliminating data transmission to third parties and supporting air-gapped environments. Fine-tuned on adversarial red-team data from CyberSecEval benchmarks, making it specifically hardened against prompt injection and jailbreak patterns.

vs alternatives

Offers finer-grained risk categorization than OpenAI's Moderation API while remaining fully open-source and deployable on-premises, though with higher latency and lower multilingual coverage than proprietary alternatives

adversarial prompt injection vulnerability detection

Medium confidence

Llama Guard 3 detects textual prompt injection attacks through classification patterns learned from CyberSecEval v2 benchmark datasets containing adversarial prompts designed to manipulate LLM behavior. The model identifies injection attempts that try to override system instructions, extract sensitive information, or trigger unintended capabilities, returning confidence scores for injection risk separate from other harm categories.

Solves for

I need to detect when users are trying to jailbreak my LLM with prompt injection techniquesI want to identify adversarial inputs that attempt to override my system prompt or extract training dataI'm testing my LLM's robustness and need automated detection of injection-based attacks in my test suite

Best for

Teams running customer-facing LLM applications vulnerable to prompt injection (chatbots, code assistants, search interfaces)

Security researchers evaluating LLM robustness against adversarial inputs

Organizations implementing defense-in-depth strategies combining multiple safety layers

Requires

Python 3.8+

PyTorch 2.0+ or inference runtime

Access to CyberSecEval benchmark datasets for validation (optional but recommended)

Limitations

Detection is pattern-based on training data; novel injection techniques not represented in CyberSecEval benchmarks may evade detection

Cannot distinguish between benign multi-turn conversations and sophisticated injection attempts that gradually escalate requests

No semantic understanding of context — may flag legitimate requests that structurally resemble injection patterns

What makes it unique

Trained specifically on CyberSecEval v2 prompt injection benchmark datasets containing real adversarial examples, rather than generic text classification. Separates injection risk from other harm categories, enabling targeted mitigation strategies. Integrated with LlamaFirewall framework for real-time scanning in production pipelines.

vs alternatives

Provides specialized injection detection trained on adversarial benchmarks, whereas generic content filters treat all policy violations equally; more effective at catching sophisticated multi-turn injection attempts than regex-based or rule-based detection systems

multi-provider llm abstraction layer for benchmark orchestration

Medium confidence

PurpleLlama's core infrastructure includes an LLM abstraction layer that provides unified interfaces for multiple LLM providers (OpenAI, Anthropic, Google, Together, Ollama) and local models. The abstraction handles provider-specific API differences, authentication, rate limiting, caching, and error handling, enabling CyberSecEval benchmarks to run against any LLM without provider-specific code. Supports both API-based and local inference with automatic fallback and retry logic.

Solves for

I need to benchmark multiple LLM providers using the same evaluation code without writing provider-specific integrationsI want to run CyberSecEval benchmarks against both cloud APIs and local models with unified configurationI'm comparing LLM security across providers and need consistent evaluation methodology

Best for

Security researchers evaluating multiple LLM providers using CyberSecEval

Teams conducting LLM procurement decisions with comparative security assessment

Organizations with multi-provider LLM deployments who need unified evaluation

Requires

Python 3.8+

API keys for target providers (OpenAI, Anthropic, Google, Together, etc.)

PurpleLlama repository with LLM abstraction layer

Limitations

Abstraction adds latency overhead (~50-100ms per request) due to wrapper layer

Provider-specific features (function calling, vision, streaming) may not be fully exposed through abstraction

Rate limiting and quota management are basic; complex multi-provider orchestration requires custom logic

What makes it unique

Provides unified abstraction for multiple LLM providers (OpenAI, Anthropic, Google, Together, Ollama) with automatic handling of API differences, rate limiting, and error handling. Enables CyberSecEval benchmarks to run against any provider without provider-specific code. Supports both cloud APIs and local inference with automatic fallback.

vs alternatives

More comprehensive provider support than LiteLLM or LangChain because it's specifically designed for security benchmarking; includes built-in caching and rate limiting for evaluation workflows

caching and batch processing for benchmark evaluation efficiency

Medium confidence

PurpleLlama's core infrastructure includes caching and batch processing mechanisms that reduce evaluation time and cost by avoiding redundant LLM API calls. The cache handler stores prompt-response pairs with provider-specific keys, enabling reuse across benchmark runs. Batch processing groups multiple prompts into single API calls where supported, reducing API overhead and improving throughput for large-scale evaluations.

Solves for

I need to run large-scale security benchmarks without incurring prohibitive API costs from redundant callsI want to speed up benchmark evaluation by caching results from previous runsI'm iterating on safety interventions and need efficient re-evaluation without re-running all benchmarks

Best for

Teams running comprehensive CyberSecEval benchmarks across multiple models and providers

Organizations conducting iterative safety intervention development with repeated evaluation

Cost-sensitive deployments where API call reduction directly impacts budget

Requires

Python 3.8+

PurpleLlama repository with caching and batch processing modules

Disk space for cache storage (varies by benchmark size)

Limitations

Cache invalidation is manual; no automatic detection of model updates or API changes

Caching assumes deterministic responses; non-deterministic models may produce different outputs for cached prompts

Batch processing is provider-specific; not all providers support batching or have the same batch size limits

What makes it unique

Provides integrated caching and batch processing specifically designed for security benchmark evaluation, with provider-aware batch size handling and cache key generation. Enables efficient re-evaluation of safety interventions without redundant API calls. Integrated with multi-provider abstraction layer for transparent caching across providers.

vs alternatives

More specialized for benchmark evaluation than generic caching solutions; provides provider-aware batch processing and cost tracking specific to security evaluation workflows

quantized model deployment for resource-constrained environments

Medium confidence

Llama Guard 3 supports multiple quantization formats (int8, int4, GPTQ) enabling deployment on edge devices, mobile platforms, and cost-constrained cloud instances with 50-75% memory reduction. The quantized models maintain classification accuracy within 1-2% of full precision while reducing inference latency by 30-40%, using post-training quantization techniques compatible with vLLM, ONNX Runtime, and TensorRT inference engines.

Solves for

I need to run safety classification on edge devices or mobile without cloud API callsI want to reduce inference costs by deploying quantized models on cheaper GPU instancesI'm building a privacy-first application and need local safety filtering with minimal resource overhead

Best for

Teams deploying LLM applications on edge devices (phones, embedded systems, IoT)

Cost-sensitive deployments on t3/t4 GPU instances or CPU-only infrastructure

Organizations with strict latency budgets requiring sub-100ms safety classification

Requires

Python 3.8+

vLLM 0.2.0+ OR ONNX Runtime 1.16+ OR TensorRT 8.5+

2GB+ VRAM for int4 quantization; 4GB+ for int8

Limitations

Quantization introduces 1-2% accuracy degradation; edge cases may be misclassified more frequently than full-precision model

int4 quantization requires specialized inference engines (GPTQ, AWQ); not all frameworks support all quantization formats

Quantized models are less flexible for fine-tuning; retraining on custom data requires full-precision base model

What makes it unique

Provides officially supported quantized variants (int8, int4) with published accuracy benchmarks, rather than requiring users to quantize themselves. Integrated with LlamaFirewall's inference abstraction layer, enabling seamless switching between quantization formats without code changes. Tested on multiple inference engines (vLLM, ONNX, TensorRT) with documented performance profiles.

vs alternatives

Offers better accuracy retention than generic quantization tools because it's trained with quantization-aware techniques; more flexible deployment options than proprietary APIs which only support cloud inference

llamafirewall integration for real-time scanning pipelines

Medium confidence

Llama Guard 3 integrates natively with LlamaFirewall, a security framework that orchestrates safety scanning across multiple stages (input scanning, output scanning, code execution monitoring). LlamaFirewall provides scanner components that wrap Llama Guard 3 classification logic with caching, batching, and policy enforcement, enabling declarative safety policies that trigger actions (block, log, escalate) based on risk thresholds without custom integration code.

Solves for

I want to deploy safety classification in my LLM pipeline without writing custom integration codeI need to enforce different safety policies for different user roles or application contextsI want to cache safety classifications to reduce latency and cost for repeated prompts

Best for

Teams building production LLM applications using Llama models with LlamaFirewall framework

Organizations needing declarative policy enforcement without custom safety orchestration code

Applications with high-volume repeated prompts where caching safety classifications provides cost savings

Requires

Python 3.8+

LlamaFirewall framework (part of PurpleLlama repository)

Llama Guard 3 model weights

Limitations

Tight coupling to LlamaFirewall framework; requires adoption of LlamaFirewall's architecture and policy DSL

Caching assumes deterministic classification; adversarial inputs designed to evade cached verdicts may bypass detection

Policy enforcement is synchronous; blocking on safety classification adds latency to every request

What makes it unique

Provides framework-level integration rather than standalone model inference, with built-in caching, batching, and declarative policy enforcement. Scanner components abstract away model-specific details, enabling swappable safety classifiers. Designed for production deployment with audit logging and compliance tracking built-in.

vs alternatives

Offers more sophisticated orchestration than calling Llama Guard 3 directly (caching, batching, policy enforcement); more flexible than hardcoded safety rules but requires adoption of LlamaFirewall framework

cybersecurity benchmark evaluation framework (cyberseceval)

Medium confidence

PurpleLlama includes CyberSecEval, a comprehensive benchmark suite for evaluating LLM security risks across multiple attack vectors: prompt injection, code interpreter abuse, vulnerability exploitation, spear phishing, and autonomous cyber operations. The framework provides standardized datasets, evaluation metrics, and orchestration code to measure LLM compliance with security frameworks (MITRE ATT&CK) and false refusal rates, enabling comparative security assessment across models and safety interventions.

Solves for

I need to benchmark my LLM's security posture against industry-standard cybersecurity evaluationsI want to measure the effectiveness of my safety interventions (guardrails, fine-tuning) using controlled adversarial datasetsI'm evaluating multiple LLM providers and need consistent security metrics for comparison

Best for

Security researchers and red-teamers evaluating LLM vulnerabilities

Teams implementing safety interventions who need quantitative effectiveness metrics

Organizations conducting LLM procurement decisions with security requirements

Requires

Python 3.8+

API keys for target LLM providers (OpenAI, Anthropic, Together, Google, etc.)

CyberSecEval benchmark datasets (included in PurpleLlama repository)

Limitations

Benchmarks are static; new attack techniques not represented in datasets will not be detected

Evaluation is time-consuming (hours to days per model depending on benchmark scope and API rate limits)

Requires API access to target models; cannot evaluate proprietary models without vendor cooperation

What makes it unique

Provides industry-first comprehensive cybersecurity evaluation framework specifically designed for LLMs, covering attack vectors (prompt injection, code interpreter abuse, vulnerability exploitation) not addressed by generic safety benchmarks. Includes MITRE ATT&CK compliance testing and false refusal rate measurement, enabling nuanced security assessment beyond binary safe/unsafe verdicts. Evolves across versions (v1, v2, v3) adding new attack categories as threats emerge.

vs alternatives

More comprehensive and adversarial-focused than generic safety benchmarks (HELM, TruthfulQA); covers cybersecurity-specific attack vectors and provides comparative metrics across multiple LLM providers

prompt injection vulnerability testing with visual and textual attack vectors

Medium confidence

CyberSecEval v2+ includes specialized benchmarks for prompt injection testing across textual and visual modalities. The framework provides datasets of adversarial prompts designed to override system instructions, extract sensitive information, or trigger unintended capabilities, plus visual prompt injection test cases (images with embedded text instructions). Evaluation measures LLM susceptibility to these attacks and tracks false refusal rates to ensure safety interventions don't over-block legitimate requests.

Solves for

I need to test whether my LLM is vulnerable to prompt injection attacks before deploying to productionI want to measure how effective my safety guardrails are at preventing injection attacks without blocking legitimate requestsI'm researching prompt injection techniques and need a standardized benchmark for comparing defense mechanisms

Best for

Security teams evaluating LLM robustness before production deployment

Researchers studying prompt injection attack and defense techniques

Teams implementing safety interventions who need quantitative effectiveness metrics

Requires

Python 3.8+

API access to target LLM (or local model inference)

CyberSecEval benchmark datasets with prompt injection test cases

Limitations

Benchmark datasets are static; novel injection techniques not in the dataset will not be tested

Visual prompt injection testing requires multimodal LLMs; not applicable to text-only models

Evaluation results are binary (vulnerable/not vulnerable); does not measure partial success or attack sophistication

What makes it unique

Provides standardized benchmark datasets for prompt injection testing across both textual and visual modalities, enabling reproducible vulnerability assessment. Includes false refusal rate measurement to ensure safety interventions don't over-block legitimate requests. Evolved from CyberSecEval v1 to v2+ with increasingly sophisticated attack patterns based on real-world jailbreak techniques.

vs alternatives

More comprehensive than ad-hoc prompt injection testing because it provides standardized datasets and metrics; covers visual injection attacks which most generic safety benchmarks ignore

code interpreter abuse and secure code generation evaluation

Medium confidence

CyberSecEval v2+ includes benchmarks for evaluating LLM security in code execution contexts, testing whether models can be manipulated to generate malicious code, exploit code interpreter vulnerabilities, or abuse code execution for unauthorized access. The framework measures both the propensity to generate insecure code and the ability to exploit vulnerabilities through code execution, with datasets covering memory corruption, privilege escalation, and data exfiltration scenarios.

Solves for

I need to assess whether my code-generation LLM can be tricked into generating malicious or exploitative codeI want to measure the security of code my LLM generates before deploying it in production code execution environmentsI'm evaluating whether my LLM can be used as a tool for offensive cybersecurity (vulnerability exploitation, privilege escalation)

Best for

Teams deploying code-generation LLMs (Copilot-like systems, code assistants) who need security assessment

Organizations running LLMs with code execution capabilities (Jupyter, code interpreters) who need risk evaluation

Security researchers studying LLM capabilities for offensive cybersecurity

Requires

Python 3.8+

API access to code-generation LLM or local model

CyberSecEval benchmark datasets with code interpreter abuse test cases

Limitations

Evaluation is limited to code generation; does not test runtime code execution vulnerabilities

Benchmark datasets are language-specific (Python, JavaScript, C); coverage of other languages is limited

Metrics are binary (secure/insecure code); does not measure code quality, performance, or functional correctness

What makes it unique

Provides specialized benchmarks for code security evaluation, covering memory corruption, privilege escalation, and data exfiltration scenarios. Measures both code generation security and exploitation capability, enabling assessment of LLM risk in code execution contexts. Integrated with CyberSecEval framework for comparative evaluation across models.

vs alternatives

More comprehensive than generic code quality metrics (linting, type checking) because it specifically targets security vulnerabilities and exploitation scenarios; more practical than manual security code review because it provides automated, reproducible evaluation

autonomous offensive cyber operations capability assessment

Medium confidence

CyberSecEval v3 includes benchmarks for evaluating LLM capability to function as autonomous agents in offensive cybersecurity scenarios, testing whether models can plan and execute multi-step cyber attacks, maintain state across attack phases, and adapt to defensive measures. The framework measures LLM ability to perform reconnaissance, exploitation, persistence, and lateral movement tasks, providing metrics for assessing autonomous cyber threat potential.

Solves for

I need to understand whether my LLM could be misused as an autonomous cyber attack toolI want to measure my LLM's capability for offensive cybersecurity tasks to inform risk mitigation strategiesI'm researching LLM capabilities in adversarial scenarios and need standardized benchmarks for comparison

Best for

Security teams evaluating LLM risk in adversarial scenarios

Organizations deploying LLMs with broad capabilities who need threat modeling

Researchers studying LLM capabilities for offensive cybersecurity

Requires

Python 3.8+

API access to target LLM or local model

CyberSecEval v3 benchmark datasets with autonomous cyber operations test cases

Limitations

Evaluation is simulated; does not test real-world attack execution against live systems

Benchmarks are limited to specific attack scenarios; novel attack strategies not in dataset will not be tested

Metrics are coarse (attack success/failure); does not measure attack sophistication or evasion capability

What makes it unique

First industry benchmark for evaluating LLM capability as autonomous cyber attack agents, covering multi-step attack planning and execution. Measures LLM ability to maintain state, adapt to defensive measures, and perform reconnaissance-to-exploitation workflows. Introduced in CyberSecEval v3 as threat landscape evolved.

vs alternatives

Unique capability assessment not available in other LLM safety benchmarks; provides forward-looking evaluation of emerging autonomous cyber threat potential

spear phishing and social engineering effectiveness evaluation

Medium confidence

CyberSecEval v3 includes benchmarks for evaluating LLM capability to generate convincing spear phishing emails and social engineering content. The framework measures LLM ability to craft personalized, contextually appropriate phishing messages, impersonate trusted entities, and exploit psychological vulnerabilities. Evaluation includes metrics for message authenticity, personalization sophistication, and social engineering success probability.

Solves for

I need to assess whether my LLM could be misused to generate convincing phishing attacksI want to measure my LLM's social engineering capability to inform security awareness training and risk mitigationI'm researching LLM-generated phishing attacks and need standardized benchmarks for defense evaluation

Best for

Security teams evaluating LLM misuse risk in social engineering scenarios

Organizations deploying LLMs with broad capabilities who need threat modeling

Researchers studying LLM-generated phishing and social engineering attacks

Requires

Python 3.8+

API access to target LLM or local model

CyberSecEval v3 benchmark datasets with spear phishing test cases

Limitations

Evaluation is simulated; does not test real-world phishing effectiveness against actual users

Metrics are subjective (authenticity, persuasiveness); requires human evaluation for reliable assessment

Benchmarks are limited to email phishing; other social engineering vectors (phone, SMS, in-person) are not covered

What makes it unique

Provides standardized benchmark for evaluating LLM capability to generate convincing spear phishing and social engineering content, including personalization and psychological manipulation. Introduced in CyberSecEval v3 to assess emerging threat of LLM-assisted social engineering attacks.

vs alternatives

Unique evaluation of LLM social engineering capability not available in other safety benchmarks; provides practical assessment of phishing generation risk

mitre att&ck framework compliance and false refusal rate measurement

Medium confidence

CyberSecEval includes benchmarks that map LLM security evaluation to the MITRE ATT&CK framework, a standardized taxonomy of adversary tactics and techniques. The framework measures LLM compliance with security best practices and false refusal rates (legitimate requests incorrectly blocked by safety systems), enabling nuanced assessment of safety intervention effectiveness. Evaluation produces compliance scores against specific MITRE techniques and tracks over-blocking that degrades user experience.

Solves for

I need to measure my LLM's compliance with security frameworks (MITRE ATT&CK) for regulatory or organizational requirementsI want to understand whether my safety guardrails are over-blocking legitimate requests and degrading user experienceI'm evaluating safety interventions and need metrics that balance security (blocking harmful requests) with usability (allowing legitimate requests)

Best for

Organizations with security compliance requirements (SOC 2, ISO 27001, etc.) who need framework-aligned evaluation

Teams implementing safety interventions who need to measure false positive rates

Security teams balancing safety and usability in LLM deployments

Requires

Python 3.8+

API access to target LLM or local model

CyberSecEval benchmark datasets with MITRE ATT&CK mappings

Limitations

MITRE ATT&CK mapping is manual and subjective; different evaluators may categorize techniques differently

False refusal rate measurement requires human judgment to distinguish legitimate from illegitimate requests

Compliance scores are coarse; does not measure partial compliance or edge cases

What makes it unique

Maps LLM security evaluation to standardized MITRE ATT&CK framework, enabling compliance assessment against industry-recognized threat taxonomy. Includes false refusal rate measurement to ensure safety interventions don't over-block legitimate requests. Provides balanced security/usability metrics rather than binary safe/unsafe verdicts.

vs alternatives

More comprehensive than generic safety metrics because it aligns with security frameworks; provides false refusal rate measurement which other benchmarks ignore, enabling usability-aware safety assessment

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 Llama Guard 3, ranked by overlap. Discovered automatically through the match graph.

Model20

Llama Guard 3 8B

Llama Guard 3 is a Llama-3.1-8B pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification)...

multi-category prompt safety classificationresponse-level content safety classification

2 shared capabilities

Dataset45

WildGuard

Allen AI's safety classification dataset and model.

response-level harm detection and classificationmulti-class prompt harmfulness classification

2 shared capabilities

Framework46

Giskard

AI testing for quality, safety, compliance — vulnerability scanning, bias/toxicity detection.

harmful content and toxicity detection in llm outputsprompt injection vulnerability scanning for llm inputs

2 shared capabilities

Framework43

Rebuff

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

llm-based semantic prompt injection detection

1 shared capability

Product21

Maxim AI

A generative AI evaluation and observability platform, empowering modern AI teams to ship products with quality, reliability, and speed.

safety and bias detection in llm outputs

1 shared capability

Product28

SydeLabs

Enhance AI security, ensure compliance, detect...

llm vulnerability scanning

1 shared capability

Best For

✓Teams deploying open-source LLMs (Llama, Mistral, etc.) who need production-grade safety without proprietary APIs
✓Organizations with strict data residency requirements who cannot use cloud-based safety services
✓Developers building specialized LLM applications (code generation, medical advice) requiring domain-specific safety tuning
✓Teams running customer-facing LLM applications vulnerable to prompt injection (chatbots, code assistants, search interfaces)
✓Security researchers evaluating LLM robustness against adversarial inputs
✓Organizations implementing defense-in-depth strategies combining multiple safety layers
✓Security researchers evaluating multiple LLM providers using CyberSecEval
✓Teams conducting LLM procurement decisions with comparative security assessment

Known Limitations

⚠Classification latency adds ~50-200ms per request depending on hardware; not suitable for sub-100ms SLA requirements
⚠Trained primarily on English text; multilingual performance degrades significantly for non-English inputs
⚠Risk categories are fixed to Meta's taxonomy; custom category detection requires fine-tuning on proprietary data
⚠No built-in context awareness — classifies individual messages in isolation without conversation history
⚠False positive rate on legitimate edge cases (e.g., discussing violence in historical/educational context) requires manual review workflows
⚠Detection is pattern-based on training data; novel injection techniques not represented in CyberSecEval benchmarks may evade detection

Requirements

Python 3.8+PyTorch 2.0+ or compatible inference runtime (ONNX, vLLM, TensorRT)4GB+ VRAM for full model inference; 2GB with quantization (int8/int4)Model weights (~7B parameters for Llama Guard 3); ~15GB disk space unquantizedPyTorch 2.0+ or inference runtimeAccess to CyberSecEval benchmark datasets for validation (optional but recommended)API keys for target providers (OpenAI, Anthropic, Google, Together, etc.)PurpleLlama repository with LLM abstraction layer

Input / Output

Accepts: plain text (user prompts, model outputs), code snippets, structured conversation turns with role labels, text prompts with potential injection patterns, multi-turn conversation histories, system prompt + user input combinations, provider name and model identifier, API credentials and configuration, prompts and requests, provider and model identifiers, cache configuration (location, TTL, batch size), text prompts, LLM prompts and outputs, code execution contexts, conversation histories, adversarial prompts from benchmark datasets, code snippets for interpreter abuse testing, vulnerability descriptions for exploitation testing, adversarial text prompts designed to trigger injection, images with embedded text instructions (for visual injection testing), multi-turn conversation histories with gradual injection escalation, prompts requesting code generation in various languages, adversarial prompts attempting to trick model into generating malicious code, code execution context descriptions (e.g., 'running in a sandboxed environment'), cyber attack scenario descriptions, network topology and system information, adversarial prompts requesting autonomous attack planning, target profile information (name, organization, role, interests), phishing scenario descriptions, adversarial prompts requesting phishing email generation, prompts mapped to MITRE ATT&CK techniques, legitimate requests that may be incorrectly blocked, security policy definitions

Produces: JSON with risk category labels and confidence scores, structured safety verdict (safe/unsafe) with category breakdown, confidence thresholds for downstream filtering logic, injection risk confidence score (0-1), category label: prompt_injection, structured explanation of detected injection pattern, unified response format across all providers, latency and cost metrics per provider, error handling and retry logs, cached responses (if available), cache hit/miss metrics, cost and latency savings from caching, JSON risk classification with category scores, latency metrics (inference time per request), policy enforcement action (allow/block/log), structured safety verdict with category breakdown, audit logs for compliance tracking, JSON evaluation results with pass/fail metrics per benchmark, compliance scores against MITRE ATT&CK framework, false refusal rate measurements, comparative security rankings across models, vulnerability assessment (vulnerable/not vulnerable), false refusal rate (% of legitimate requests blocked), attack success metrics (% of injection attempts that succeeded), code security assessment (secure/insecure), vulnerability exploitation success rate, false positive rate (% of legitimate security code flagged as malicious), attack planning capability assessment, multi-step attack execution success rate, evasion and adaptation capability metrics, phishing email generation capability assessment, message authenticity and personalization scores, social engineering success probability estimates, MITRE ATT&CK compliance scores per technique, compliance report for regulatory/organizational requirements

UnfragileRank

Adoption70%(40% weight)

Quality23%(20% weight)

Ecosystem30%(15% weight)

Match Graph10%(20% 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: Model

12 capabilities

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About

Meta's safety classifier model that detects harmful content in LLM inputs and outputs across multiple risk categories including violence, sexual content, and criminal planning, designed to be deployed as a guardrail layer.

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Capabilities12 decomposed

multi-category harmful content classification for llm inputs and outputs

Medium confidence

Solves for

Best for

Teams deploying open-source LLMs (Llama, Mistral, etc.) who need production-grade safety without proprietary APIs

Organizations with strict data residency requirements who cannot use cloud-based safety services

Developers building specialized LLM applications (code generation, medical advice) requiring domain-specific safety tuning

Requires

Python 3.8+

PyTorch 2.0+ or compatible inference runtime (ONNX, vLLM, TensorRT)

4GB+ VRAM for full model inference; 2GB with quantization (int8/int4)

Limitations

Classification latency adds ~50-200ms per request depending on hardware; not suitable for sub-100ms SLA requirements

Trained primarily on English text; multilingual performance degrades significantly for non-English inputs

Risk categories are fixed to Meta's taxonomy; custom category detection requires fine-tuning on proprietary data

What makes it unique

vs alternatives

adversarial prompt injection vulnerability detection

Medium confidence

Solves for

Best for

Teams running customer-facing LLM applications vulnerable to prompt injection (chatbots, code assistants, search interfaces)

Security researchers evaluating LLM robustness against adversarial inputs

Organizations implementing defense-in-depth strategies combining multiple safety layers

Requires

Python 3.8+

PyTorch 2.0+ or inference runtime

Access to CyberSecEval benchmark datasets for validation (optional but recommended)

Limitations

Detection is pattern-based on training data; novel injection techniques not represented in CyberSecEval benchmarks may evade detection

Cannot distinguish between benign multi-turn conversations and sophisticated injection attempts that gradually escalate requests

No semantic understanding of context — may flag legitimate requests that structurally resemble injection patterns

What makes it unique

vs alternatives

multi-provider llm abstraction layer for benchmark orchestration

Medium confidence

Solves for

Best for

Security researchers evaluating multiple LLM providers using CyberSecEval

Teams conducting LLM procurement decisions with comparative security assessment

Organizations with multi-provider LLM deployments who need unified evaluation

Requires

Python 3.8+

API keys for target providers (OpenAI, Anthropic, Google, Together, etc.)

PurpleLlama repository with LLM abstraction layer

Limitations

Abstraction adds latency overhead (~50-100ms per request) due to wrapper layer

Provider-specific features (function calling, vision, streaming) may not be fully exposed through abstraction

Rate limiting and quota management are basic; complex multi-provider orchestration requires custom logic

What makes it unique

vs alternatives

More comprehensive provider support than LiteLLM or LangChain because it's specifically designed for security benchmarking; includes built-in caching and rate limiting for evaluation workflows

caching and batch processing for benchmark evaluation efficiency

Medium confidence

Solves for

Best for

Teams running comprehensive CyberSecEval benchmarks across multiple models and providers

Organizations conducting iterative safety intervention development with repeated evaluation

Cost-sensitive deployments where API call reduction directly impacts budget

Requires

Python 3.8+

PurpleLlama repository with caching and batch processing modules

Disk space for cache storage (varies by benchmark size)

Limitations

Cache invalidation is manual; no automatic detection of model updates or API changes

Caching assumes deterministic responses; non-deterministic models may produce different outputs for cached prompts

Batch processing is provider-specific; not all providers support batching or have the same batch size limits

What makes it unique

vs alternatives

More specialized for benchmark evaluation than generic caching solutions; provides provider-aware batch processing and cost tracking specific to security evaluation workflows

quantized model deployment for resource-constrained environments

Medium confidence

Solves for

Best for

Teams deploying LLM applications on edge devices (phones, embedded systems, IoT)

Cost-sensitive deployments on t3/t4 GPU instances or CPU-only infrastructure

Organizations with strict latency budgets requiring sub-100ms safety classification

Requires

Python 3.8+

vLLM 0.2.0+ OR ONNX Runtime 1.16+ OR TensorRT 8.5+

2GB+ VRAM for int4 quantization; 4GB+ for int8

Limitations

Quantization introduces 1-2% accuracy degradation; edge cases may be misclassified more frequently than full-precision model

int4 quantization requires specialized inference engines (GPTQ, AWQ); not all frameworks support all quantization formats

Quantized models are less flexible for fine-tuning; retraining on custom data requires full-precision base model

What makes it unique

vs alternatives

llamafirewall integration for real-time scanning pipelines

Medium confidence

Solves for

Best for

Teams building production LLM applications using Llama models with LlamaFirewall framework

Organizations needing declarative policy enforcement without custom safety orchestration code

Applications with high-volume repeated prompts where caching safety classifications provides cost savings

Requires

Python 3.8+

LlamaFirewall framework (part of PurpleLlama repository)

Llama Guard 3 model weights

Limitations

Tight coupling to LlamaFirewall framework; requires adoption of LlamaFirewall's architecture and policy DSL

Caching assumes deterministic classification; adversarial inputs designed to evade cached verdicts may bypass detection

Policy enforcement is synchronous; blocking on safety classification adds latency to every request

What makes it unique

vs alternatives

cybersecurity benchmark evaluation framework (cyberseceval)

Medium confidence

Solves for

Best for

Security researchers and red-teamers evaluating LLM vulnerabilities

Teams implementing safety interventions who need quantitative effectiveness metrics

Organizations conducting LLM procurement decisions with security requirements

Requires

Python 3.8+

API keys for target LLM providers (OpenAI, Anthropic, Together, Google, etc.)

CyberSecEval benchmark datasets (included in PurpleLlama repository)

Limitations

Benchmarks are static; new attack techniques not represented in datasets will not be detected

Evaluation is time-consuming (hours to days per model depending on benchmark scope and API rate limits)

Requires API access to target models; cannot evaluate proprietary models without vendor cooperation

What makes it unique

vs alternatives

prompt injection vulnerability testing with visual and textual attack vectors

Medium confidence

Solves for

Best for

Security teams evaluating LLM robustness before production deployment

Researchers studying prompt injection attack and defense techniques

Teams implementing safety interventions who need quantitative effectiveness metrics

Requires

Python 3.8+

API access to target LLM (or local model inference)

CyberSecEval benchmark datasets with prompt injection test cases

Limitations

Benchmark datasets are static; novel injection techniques not in the dataset will not be tested

Visual prompt injection testing requires multimodal LLMs; not applicable to text-only models

Evaluation results are binary (vulnerable/not vulnerable); does not measure partial success or attack sophistication

What makes it unique

vs alternatives

More comprehensive than ad-hoc prompt injection testing because it provides standardized datasets and metrics; covers visual injection attacks which most generic safety benchmarks ignore

code interpreter abuse and secure code generation evaluation

Medium confidence

Solves for

Best for

Teams deploying code-generation LLMs (Copilot-like systems, code assistants) who need security assessment

Organizations running LLMs with code execution capabilities (Jupyter, code interpreters) who need risk evaluation

Security researchers studying LLM capabilities for offensive cybersecurity

Requires

Python 3.8+

API access to code-generation LLM or local model

CyberSecEval benchmark datasets with code interpreter abuse test cases

Limitations

Evaluation is limited to code generation; does not test runtime code execution vulnerabilities

Benchmark datasets are language-specific (Python, JavaScript, C); coverage of other languages is limited

Metrics are binary (secure/insecure code); does not measure code quality, performance, or functional correctness

What makes it unique

vs alternatives

autonomous offensive cyber operations capability assessment

Medium confidence

Solves for

Best for

Security teams evaluating LLM risk in adversarial scenarios

Organizations deploying LLMs with broad capabilities who need threat modeling

Researchers studying LLM capabilities for offensive cybersecurity

Requires

Python 3.8+

API access to target LLM or local model

CyberSecEval v3 benchmark datasets with autonomous cyber operations test cases

Limitations

Evaluation is simulated; does not test real-world attack execution against live systems

Benchmarks are limited to specific attack scenarios; novel attack strategies not in dataset will not be tested

Metrics are coarse (attack success/failure); does not measure attack sophistication or evasion capability

What makes it unique

vs alternatives

Unique capability assessment not available in other LLM safety benchmarks; provides forward-looking evaluation of emerging autonomous cyber threat potential

spear phishing and social engineering effectiveness evaluation

Medium confidence

Solves for

Best for

Security teams evaluating LLM misuse risk in social engineering scenarios

Organizations deploying LLMs with broad capabilities who need threat modeling

Researchers studying LLM-generated phishing and social engineering attacks

Requires

Python 3.8+

API access to target LLM or local model

CyberSecEval v3 benchmark datasets with spear phishing test cases

Limitations

Evaluation is simulated; does not test real-world phishing effectiveness against actual users

Metrics are subjective (authenticity, persuasiveness); requires human evaluation for reliable assessment

Benchmarks are limited to email phishing; other social engineering vectors (phone, SMS, in-person) are not covered

What makes it unique

vs alternatives

Unique evaluation of LLM social engineering capability not available in other safety benchmarks; provides practical assessment of phishing generation risk

mitre att&ck framework compliance and false refusal rate measurement

Medium confidence

Solves for

Best for

Organizations with security compliance requirements (SOC 2, ISO 27001, etc.) who need framework-aligned evaluation

Teams implementing safety interventions who need to measure false positive rates

Security teams balancing safety and usability in LLM deployments

Requires

Python 3.8+

API access to target LLM or local model

CyberSecEval benchmark datasets with MITRE ATT&CK mappings

Limitations

MITRE ATT&CK mapping is manual and subjective; different evaluators may categorize techniques differently

False refusal rate measurement requires human judgment to distinguish legitimate from illegitimate requests

Compliance scores are coarse; does not measure partial compliance or edge cases

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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Llama Guard 3

Capabilities12 decomposed

multi-category harmful content classification for llm inputs and outputs

adversarial prompt injection vulnerability detection

multi-provider llm abstraction layer for benchmark orchestration

caching and batch processing for benchmark evaluation efficiency

quantized model deployment for resource-constrained environments

llamafirewall integration for real-time scanning pipelines

cybersecurity benchmark evaluation framework (cyberseceval)

prompt injection vulnerability testing with visual and textual attack vectors

code interpreter abuse and secure code generation evaluation

autonomous offensive cyber operations capability assessment

spear phishing and social engineering effectiveness evaluation

mitre att&ck framework compliance and false refusal rate measurement

Related Artifactssharing capabilities

Llama Guard 3 8B

WildGuard

Giskard

Rebuff

Maxim AI

SydeLabs

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to Llama Guard 3

Are you the builder of Llama Guard 3?

Get the weekly brief

Data Sources

Llama Guard 3

Capabilities12 decomposed

multi-category harmful content classification for llm inputs and outputs

adversarial prompt injection vulnerability detection

multi-provider llm abstraction layer for benchmark orchestration

caching and batch processing for benchmark evaluation efficiency

quantized model deployment for resource-constrained environments

llamafirewall integration for real-time scanning pipelines

cybersecurity benchmark evaluation framework (cyberseceval)

prompt injection vulnerability testing with visual and textual attack vectors

code interpreter abuse and secure code generation evaluation

autonomous offensive cyber operations capability assessment

spear phishing and social engineering effectiveness evaluation

mitre att&ck framework compliance and false refusal rate measurement

Related Artifactssharing capabilities

Llama Guard 3 8B

WildGuard

Giskard

Rebuff

Maxim AI

SydeLabs

Best For

Known Limitations

Requirements

Input / Output

UnfragileRank

About

Categories

Alternatives to Llama Guard 3

Are you the builder of Llama Guard 3?

Get the weekly brief

Data Sources