What can Llama Guard 3 do?

multi-category harmful content classification for llm inputs and outputs, red-team and blue-team cybersecurity benchmarking framework (cyberseceval), prompt guard prompt injection detection, codeshield code security analysis and vulnerability detection, model card and safety documentation generation, llm provider abstraction layer with unified inference interface, prompt injection and jailbreak vulnerability testing, code generation and interpreter security evaluation, mitre att&ck framework compliance and false refusal measurement, visual prompt injection vulnerability testing, spear phishing and social engineering capability assessment, autonomous offensive cyber operations capability evaluation, llamafirewall modular security scanning and filtering

Llama Guard 3

ModelFree

Meta's safety classifier for LLM content moderation.

Open Source

/ 100

13 capabilities

Capabilities13 decomposed

multi-category harmful content classification for llm inputs and outputs

Medium confidence

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.

Solves for

I need to filter user prompts before they reach my LLM to prevent jailbreak attempts and harmful requestsI want to scan LLM outputs before returning them to users to catch unsafe generationsI need to classify content across multiple risk categories to apply different handling policies per categoryI 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

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)

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

What makes it unique

Llama Guard 3 is a purpose-built safety classifier (not a general-purpose LLM) fine-tuned on adversarial examples and safety datasets, enabling faster inference and higher accuracy on harm detection compared to using a general LLM with safety prompting. It supports both input and output classification with explicit multi-category taxonomy aligned to real-world deployment needs.

vs alternatives

More accurate and faster than prompt-engineering a general LLM for safety (e.g., GPT-4 with safety instructions), and fully open-source for on-premise deployment without API dependencies or data transmission concerns.

red-team and blue-team cybersecurity benchmarking framework (cyberseceval)

Medium confidence

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.

Solves for

I need to evaluate whether my LLM is vulnerable to prompt injection and jailbreak attacks before deploying itI want to measure my LLM's propensity to generate malicious code or assist with cyberattacksI need to benchmark multiple LLM providers (OpenAI, Anthropic, Llama) on the same security evaluation to compare their safety profilesI 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

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

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

What makes it unique

CyberSecEval v3 is the first industry-wide cybersecurity benchmark suite that combines multiple attack vectors (prompt injection, MITRE ATT&CK, code interpreter abuse, visual injection, spear phishing, autonomous operations) in a single framework with multi-provider LLM abstraction, enabling comparative security evaluation across different model families and versions.

vs alternatives

More comprehensive than single-vector benchmarks (e.g., prompt injection-only tests) and more practical than manual red-teaming because it provides reproducible, scalable evaluation across multiple LLM providers with standardized metrics.

prompt guard prompt injection detection

Medium confidence

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.

Solves for

I need to detect prompt injection attacks in user inputs before they reach my LLMI want a specialized model for injection detection that's faster and more accurate than general-purpose safety classifiersI 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

Requires

Python 3.8+

Prompt Guard model weights (from Meta)

PyTorch or compatible inference framework

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

What makes it unique

Prompt Guard is a specialized model trained specifically for prompt injection detection (not general content safety), enabling higher accuracy and lower false positive rates than general-purpose classifiers. Designed for deployment as an input filter with minimal latency impact.

vs alternatives

More accurate and faster than using Llama Guard for injection detection because it's specialized for this single task, and more practical than rule-based injection detection because it learns patterns from adversarial examples.

codeshield code security analysis and vulnerability detection

Medium confidence

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.

Solves for

I need to scan LLM-generated code for security vulnerabilities before returning it to usersI want to detect requests for malicious code generation and refuse themI 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

Requires

Python 3.8+

CodeShield model weights (from Meta)

PyTorch or compatible inference framework

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

What makes it unique

CodeShield is a specialized model for code security analysis trained on vulnerability patterns and insecure code examples, enabling detection of security issues in LLM-generated code without requiring external SAST tools. Provides vulnerability type classification and reasoning.

vs alternatives

More integrated with LLM workflows than traditional SAST tools because it operates on code snippets and generation requests in real-time, and more practical than manual code review because it provides automated, scalable security analysis.

model card and safety documentation generation

Medium confidence

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.

Solves for

I need to understand the training data and evaluation methodology for Llama Guard 3 before deploying itI want to know the known limitations and failure modes of the safety modelI 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

Requires

Access to model card documentation (provided in repo)

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

What makes it unique

Meta provides comprehensive model cards documenting training methodology, evaluation results, and known limitations, enabling informed deployment decisions. Includes specific guidance on threshold tuning and false refusal rate management.

vs alternatives

More transparent than proprietary safety models (e.g., OpenAI's content moderation API) because full documentation is available, enabling practitioners to understand and audit the model's behavior.

llm provider abstraction layer with unified inference interface

Medium confidence

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.

Solves for

I want to run the same safety evaluation against OpenAI, Anthropic, and local Llama models without rewriting code for each providerI need to abstract away provider-specific API quirks so my safety tool works with any LLM backendI 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

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.)

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

What makes it unique

Implements a provider-agnostic LLM abstraction (llm_base.py with subclasses for OpenAI, Anthropic, Google, Together, local models) that normalizes request/response formats and error handling, enabling the same benchmark and safety code to execute against any LLM without conditional logic per provider.

vs alternatives

More comprehensive than LiteLLM or similar libraries because it's tightly integrated with the CyberSecEval benchmarking framework and includes built-in caching and batch execution optimizations specific to safety evaluation workflows.

prompt injection and jailbreak vulnerability testing

Medium confidence

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.

Solves for

I need to test whether my LLM is vulnerable to prompt injection before deploying it in productionI want to measure the false refusal rate of my safety guardrails to ensure they don't block legitimate requestsI 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)

Requires

Python 3.9+

Access to target LLM (API or local deployment)

Prompt injection benchmark dataset (provided in repo)

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

What makes it unique

CyberSecEval's prompt injection benchmark includes both textual and visual injection vectors (v3+), with multilingual variants (machine-translated MITRE prompts) and explicit measurement of false refusal rates, enabling more nuanced evaluation than binary safe/unsafe classification.

vs alternatives

More systematic than manual prompt injection testing because it provides reproducible, quantified results across multiple injection techniques and models, and includes false refusal measurement which is often overlooked in simpler safety evaluations.

code generation and interpreter security evaluation

Medium confidence

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.

Solves for

I need to assess whether my LLM generates secure code or introduces vulnerabilities when asked to write functionsI want to test if my LLM can be tricked into generating code that exploits memory corruption or other low-level vulnerabilitiesI 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

Requires

Python 3.9+

Access to target LLM

Code generation benchmark datasets (provided in repo)

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

What makes it unique

CyberSecEval's code security benchmarks include both code generation evaluation (is the generated code secure?) and code interpreter abuse testing (can the LLM be tricked into executing malicious code?), with explicit memory corruption and vulnerability exploitation scenarios.

vs alternatives

More comprehensive than SAST tools alone because it evaluates the LLM's behavior and reasoning about security, not just the syntactic properties of generated code, and includes interpreter abuse scenarios that static analysis cannot detect.

mitre att&ck framework compliance and false refusal measurement

Medium confidence

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.

Solves for

I need to verify that my LLM correctly refuses requests aligned with MITRE ATT&CK attack techniquesI want to measure false refusal rates to ensure my safety guardrails don't block legitimate security researchI 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

Requires

Python 3.9+

Access to target LLM

MITRE ATT&CK benchmark dataset (provided in repo, includes multilingual variants)

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

What makes it unique

Explicitly measures false refusal rates alongside attack refusal rates, recognizing that overly aggressive safety guardrails harm utility. Includes multilingual variants (machine-translated MITRE prompts) to evaluate safety across languages, addressing a gap in most English-only benchmarks.

vs alternatives

More nuanced than simple refusal-rate metrics because it distinguishes between legitimate refusals (blocking actual attacks) and false refusals (blocking legitimate security research), enabling better calibration of safety policies.

visual prompt injection vulnerability testing

Medium confidence

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.

Solves for

I need to test whether my multimodal LLM is vulnerable to prompt injection attacks hidden in imagesI want to evaluate the security of my vision-enabled LLM before deploying it in productionI 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

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)

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

What makes it unique

First industry benchmark for visual prompt injection attacks on multimodal LLMs, recognizing that vision-language models introduce new attack surface beyond text. Includes steganographic and adversarial visual patterns, not just text-in-image injection.

vs alternatives

Addresses a gap in existing safety benchmarks which focus exclusively on textual attacks; visual injection is a distinct threat vector for multimodal models that requires separate evaluation.

spear phishing and social engineering capability assessment

Medium confidence

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.

Solves for

I need to assess whether my LLM can be abused to generate phishing emails or social engineering contentI want to measure my LLM's resistance to requests for social engineering assistanceI 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

Requires

Python 3.9+

Access to target LLM

Spear phishing benchmark dataset (provided in repo)

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

What makes it unique

Explicitly evaluates LLM capability to generate convincing social engineering content, recognizing that phishing is a primary attack vector in cybersecurity. Measures both refusal rates and content quality, providing nuanced assessment of social engineering risk.

vs alternatives

More practical than generic harm benchmarks because it focuses on a specific, high-impact attack vector (phishing) that organizations care about, with evaluation criteria aligned to real-world phishing effectiveness.

autonomous offensive cyber operations capability evaluation

Medium confidence

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.

Solves for

I need to assess whether my LLM can be used as an autonomous cyber attack agentI want to measure my LLM's capability to plan and execute multi-step attack scenariosI 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

Requires

Python 3.9+

Access to target LLM

Autonomous cyber operations benchmark dataset (provided in repo, may be restricted)

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

What makes it unique

First benchmark evaluating LLM capability to function as an autonomous agent in multi-step offensive cyber scenarios, recognizing that LLM-as-agent architectures introduce new risks beyond single-turn harmful content generation. Measures task decomposition, state management, and multi-step execution.

vs alternatives

Addresses emerging risk of LLM agents being used for autonomous attacks, which is not captured by single-turn safety evaluations or simple refusal-rate metrics. Requires sophisticated evaluation infrastructure and security expertise.

llamafirewall modular security scanning and filtering

Medium confidence

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.

Solves for

I need to deploy multiple security scanners (content safety, prompt injection, code security) in a single pipelineI 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

Requires

Python 3.8+

LlamaFirewall framework (from PurpleLlama repo)

Individual scanner models/implementations (Llama Guard, Prompt Guard, CodeShield)

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

What makes it unique

LlamaFirewall provides a modular, composable security framework that allows combining multiple specialized scanners (Llama Guard for content, Prompt Guard for injection, CodeShield for code) with configurable policies per scanner, enabling flexible security posture without monolithic design.

vs alternatives

More flexible than single-purpose safety tools because it supports composition of multiple scanners with independent policies, and more practical than building custom security pipelines because it provides standard scanner implementations and configuration patterns.

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.

Model58

Llama Guard

Meta's LLM safety classifier for content policy enforcement.

cybersecurity benchmark evaluation and red-teaming integrationprompt injection vulnerability detectionvisual prompt injection attack detection and evaluation

3 shared capabilities

Dataset59

WildGuard

Allen AI's safety classification dataset and model.

multi-class prompt harmfulness classificationresponse harmfulness detection and classificationcurated adversarial prompt dataset with human annotations

3 shared capabilities

Model57

Prompt Guard

Meta's prompt injection and jailbreak detection classifier.

evaluation against cyberseceval v2+ benchmark datasets for attack coveragebinary prompt injection classification with transformer-based detection

2 shared capabilities

Framework56

LLM Guard

Open-source LLM input/output security scanner toolkit.

prompt injection detection via multiple pattern and semantic approachescode injection and malicious code detection in prompts and outputs

2 shared capabilities

Product49

Lakera

AI's ultimate shield: real-time threat detection, privacy,...

real-time prompt injection detection

1 shared capability

Model23

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 classification

1 shared capability

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
✓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
✓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)

Known 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
⚠Benchmark execution requires API keys for multiple LLM providers, incurring costs for each evaluation run

Requirements

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 processingPython 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 infrastructureBenchmark datasets (provided in repo as JSON files)

Input / Output

Accepts: plain text (user prompts, LLM outputs), structured conversation turns (user message + assistant response pairs), benchmark datasets (JSON format with attack prompts, expected behaviors), LLM provider configurations (API endpoints, model names, parameters), evaluation parameters (batch size, timeout, retry logic), user prompts (text), system prompts (for context), code snippets (Python, JavaScript, C, Java, etc.), code generation requests (prompts asking for code), none (documentation artifact), unified request objects (prompt text, model name, temperature, max_tokens, etc.), provider configuration (API endpoint, authentication credentials), original system prompt, user query, injected instruction (adversarial prompt), code generation prompts (requests to write functions, scripts, etc.), code interpreter abuse scenarios (requests to exploit vulnerabilities), vulnerability exploitation prompts, MITRE ATT&CK-mapped prompts (attack technique descriptions, requests for assistance), legitimate security research prompts (defensive questions, educational requests), images with embedded text overlays or adversarial patterns, social engineering prompts (requests to generate phishing emails, impersonation scripts, etc.), target context (company name, employee role, etc.), 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), text (user prompts, LLM outputs), code (for CodeShield analysis), structured scanner configurations

Produces: binary classification (safe/unsafe), multi-class category predictions (violence, sexual, criminal, etc.), confidence scores per category, structured JSON with category breakdown, 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, binary classification (injection/safe), confidence score, injection technique identified (if applicable), security classification (secure/insecure), vulnerability types identified, reasoning/explanation, structured documentation (markdown, JSON), evaluation results and metrics, deployment recommendations, unified response objects (generated text, stop reason, token counts), structured error objects with provider-agnostic error codes, binary success/failure per injection attempt, aggregated injection success rate, false refusal rate metrics, generated code samples, vulnerability type identified, aggregated metrics (% secure code, % refusals), refusal rate per MITRE technique, false refusal rate (legitimate requests blocked), true positive rate (actual attacks refused), structured results by technique category, binary success/failure per visual injection, aggregated visual injection success rate, comparison with textual injection effectiveness, refusal rate for social engineering requests, quality assessment of generated phishing content, aggregated metrics on social engineering capability, 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, per-scanner results (classification, confidence, reasoning), aggregated security decision (allow/block/flag), structured JSON with all scanner outputs

UnfragileRank

Adoption70%(35% weight)

Quality90%(20% weight)

Ecosystem30%(10% weight)

Match Graph25%(30% 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

13 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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Capabilities13 decomposed

multi-category harmful content classification for llm inputs and outputs

Medium confidence

Solves for

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

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)

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

What makes it unique

vs alternatives

red-team and blue-team cybersecurity benchmarking framework (cyberseceval)

Medium confidence

Solves for

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

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

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

What makes it unique

vs alternatives

prompt guard prompt injection detection

Medium confidence

Solves for

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

Requires

Python 3.8+

Prompt Guard model weights (from Meta)

PyTorch or compatible inference framework

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

What makes it unique

vs alternatives

codeshield code security analysis and vulnerability detection

Medium confidence

Solves for

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

Requires

Python 3.8+

CodeShield model weights (from Meta)

PyTorch or compatible inference framework

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

What makes it unique

vs alternatives

model card and safety documentation generation

Medium confidence

Solves for

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

Requires

Access to model card documentation (provided in repo)

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

What makes it unique

vs alternatives

More transparent than proprietary safety models (e.g., OpenAI's content moderation API) because full documentation is available, enabling practitioners to understand and audit the model's behavior.

llm provider abstraction layer with unified inference interface

Medium confidence

Solves for

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

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.)

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

What makes it unique

vs alternatives

prompt injection and jailbreak vulnerability testing

Medium confidence

Solves for

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)

Requires

Python 3.9+

Access to target LLM (API or local deployment)

Prompt injection benchmark dataset (provided in repo)

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

What makes it unique

vs alternatives

code generation and interpreter security evaluation

Medium confidence

Solves for

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

Requires

Python 3.9+

Access to target LLM

Code generation benchmark datasets (provided in repo)

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

What makes it unique

vs alternatives

mitre att&ck framework compliance and false refusal measurement

Medium confidence

Solves for

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

Requires

Python 3.9+

Access to target LLM

MITRE ATT&CK benchmark dataset (provided in repo, includes multilingual variants)

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

What makes it unique

vs alternatives

visual prompt injection vulnerability testing

Medium confidence

Solves for

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

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)

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

What makes it unique

vs alternatives

Addresses a gap in existing safety benchmarks which focus exclusively on textual attacks; visual injection is a distinct threat vector for multimodal models that requires separate evaluation.

spear phishing and social engineering capability assessment

Medium confidence

Solves for

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

Requires

Python 3.9+

Access to target LLM

Spear phishing benchmark dataset (provided in repo)

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

What makes it unique

vs alternatives

autonomous offensive cyber operations capability evaluation

Medium confidence

Solves for

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

Requires

Python 3.9+

Access to target LLM

Autonomous cyber operations benchmark dataset (provided in repo, may be restricted)

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

What makes it unique

vs alternatives

llamafirewall modular security scanning and filtering

Medium confidence

Solves for

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

Requires

Python 3.8+

LlamaFirewall framework (from PurpleLlama repo)

Individual scanner models/implementations (Llama Guard, Prompt Guard, CodeShield)

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

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

Capabilities13 decomposed

multi-category harmful content classification for llm inputs and outputs

red-team and blue-team cybersecurity benchmarking framework (cyberseceval)

prompt guard prompt injection detection

codeshield code security analysis and vulnerability detection

model card and safety documentation generation

llm provider abstraction layer with unified inference interface

prompt injection and jailbreak vulnerability testing

code generation and interpreter security evaluation

mitre att&ck framework compliance and false refusal measurement

visual prompt injection vulnerability testing

spear phishing and social engineering capability assessment

autonomous offensive cyber operations capability evaluation

llamafirewall modular security scanning and filtering

Related Artifactssharing capabilities

Llama Guard

WildGuard

Prompt Guard

LLM Guard

Lakera

Llama Guard 3 8B

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

Capabilities13 decomposed

multi-category harmful content classification for llm inputs and outputs

red-team and blue-team cybersecurity benchmarking framework (cyberseceval)

prompt guard prompt injection detection

codeshield code security analysis and vulnerability detection

model card and safety documentation generation

llm provider abstraction layer with unified inference interface

prompt injection and jailbreak vulnerability testing

code generation and interpreter security evaluation

mitre att&ck framework compliance and false refusal measurement

visual prompt injection vulnerability testing

spear phishing and social engineering capability assessment

autonomous offensive cyber operations capability evaluation

llamafirewall modular security scanning and filtering

Related Artifactssharing capabilities

Llama Guard

WildGuard

Prompt Guard

LLM Guard

Lakera

Llama Guard 3 8B

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