Prompt Guard
ModelFreeMeta's prompt injection and jailbreak detection classifier.
Capabilities9 decomposed
binary prompt injection classification with transformer-based detection
Medium confidencePrompt Guard implements a lightweight transformer-based binary classifier that analyzes input text to detect prompt injection and jailbreak attempts before they reach the target LLM. The model uses a fine-tuned encoder architecture trained on adversarial prompt datasets to distinguish between benign user inputs and malicious injection patterns, operating as a preprocessing filter that can be deployed independently of the underlying LLM provider.
Part of Meta's Purple Llama project combining red-team (adversarial) and blue-team (defensive) approaches; trained on CyberSecEval v2+ benchmark datasets that include MITRE-mapped prompt injection attacks and visual prompt injection patterns, providing broader coverage than single-source training data
Provides open-source, deployable-anywhere binary classification versus closed-source API-dependent solutions, with training grounded in comprehensive cybersecurity benchmarks rather than ad-hoc datasets
multilingual prompt injection detection with machine-translated adversarial datasets
Medium confidencePrompt Guard extends injection detection across multiple languages by leveraging machine-translated versions of adversarial prompt datasets from the CyberSecEval benchmarks. The model processes non-English inputs through the same transformer encoder, enabling detection of injection attempts crafted in languages other than English without requiring separate language-specific models or retraining.
Leverages CyberSecEval's multilingual dataset (mitre_prompts_multilingual_machine_translated.json) to provide single-model multilingual detection rather than language-specific classifiers, reducing deployment complexity while acknowledging translation-based limitations
Single unified model for multiple languages versus maintaining separate classifiers per language; trades off native-speaker accuracy for operational simplicity and consistency
integration with llamafirewall scanner pipeline for layered defense
Medium confidencePrompt Guard operates as a component within the broader LlamaFirewall security framework, which orchestrates multiple scanner modules (including Prompt Guard, Llama Guard for output filtering, and CodeShield for code-specific threats) into a coordinated defense pipeline. The architecture allows Prompt Guard to be deployed as the first-stage input filter, with results passed to downstream scanners for comprehensive threat assessment across the full LLM interaction lifecycle.
Designed as a modular component within LlamaFirewall's scanner architecture, enabling composition with Llama Guard (output filtering) and CodeShield (code threat detection) in a coordinated pipeline rather than standalone deployment
Provides architectural integration with complementary safeguards versus point solutions that require custom orchestration; enables defense-in-depth but requires more setup than standalone classifiers
evaluation against cyberseceval v2+ benchmark datasets for attack coverage
Medium confidencePrompt Guard's detection capabilities are grounded in and evaluated against the CyberSecEval benchmark suite, which includes MITRE-mapped prompt injection tests, visual prompt injection attacks, and adversarial patterns from multiple attack categories. The model's performance is measured against these standardized benchmarks, providing transparency into which attack types it can detect and which remain out-of-scope, enabling users to understand coverage gaps and make informed deployment decisions.
Trained and evaluated against CyberSecEval v2+ which includes MITRE-mapped attack categories, visual prompt injection, and autonomous offensive cyber operations — broader threat coverage than single-category injection detection benchmarks
Provides transparent, reproducible evaluation against industry-standard benchmarks versus proprietary evaluation claims; enables users to understand specific attack coverage rather than generic 'accuracy' metrics
lightweight inference for low-latency preprocessing in request pipelines
Medium confidencePrompt Guard is optimized as a lightweight model (~1B parameters) designed for real-time inference in request preprocessing pipelines, with minimal latency overhead added to LLM API calls. The model uses efficient transformer architecture patterns (likely distilled or pruned variants) to enable sub-100ms inference on standard hardware, allowing deployment as a synchronous preprocessing step without requiring asynchronous queuing or significant infrastructure investment.
Designed as a ~1B parameter model optimized for real-time inference in synchronous request pipelines, enabling deployment as a preprocessing step without asynchronous queuing or significant infrastructure overhead
Faster inference than larger safeguard models (e.g., Llama Guard 2 at 7B parameters) enabling synchronous preprocessing; trades off potential accuracy gains from larger models for operational simplicity and latency
configurable detection thresholds for precision-recall tradeoff tuning
Medium confidencePrompt Guard outputs logits or confidence scores (in addition to binary classification) that can be thresholded to adjust the precision-recall tradeoff based on application requirements. Users can configure detection sensitivity to prioritize either false-positive reduction (higher threshold, fewer blocks) or false-negative reduction (lower threshold, more blocks), enabling tuning for specific threat models and user experience requirements without retraining.
Exposes confidence scores enabling threshold-based tuning without retraining, allowing users to calibrate detection sensitivity to their specific precision-recall requirements and threat model
Provides post-hoc tuning capability versus fixed binary classifiers; enables operational flexibility but requires more sophisticated deployment infrastructure than simple true/false filtering
model card documentation with threat model and evaluation methodology
Medium confidencePrompt Guard includes comprehensive model card documentation (MODEL_CARD.md in repository) that specifies the threat model, training data sources, evaluation methodology, performance metrics, and known limitations. This documentation enables users to understand the model's design assumptions, evaluate its suitability for their use case, and make informed decisions about deployment and complementary safeguards.
Provides comprehensive model card grounded in Purple Llama's purple-team (red+blue) approach, documenting both adversarial attack patterns (red team) and defensive evaluation methodology (blue team)
Open-source model card versus proprietary safeguards with minimal documentation; enables informed evaluation but requires users to interpret technical documentation
open-source model weights and inference code for self-hosted deployment
Medium confidencePrompt Guard is released as open-source with publicly available model weights and inference code, enabling users to download, inspect, and deploy the model in their own infrastructure without reliance on external APIs or vendor lock-in. The model can be deployed on-premises, in private cloud environments, or at the edge, with full control over data flow and inference infrastructure.
Open-source release with full model weights and inference code as part of Meta's Purple Llama project, enabling self-hosted deployment versus proprietary API-only safeguards
Full transparency and control versus managed API services; requires more operational overhead but eliminates vendor lock-in and data transmission to external services
integration with llm provider abstraction layer for multi-provider evaluation
Medium confidencePrompt Guard is evaluated and can be integrated with the Purple Llama LLM abstraction layer, which provides unified interfaces to multiple LLM providers (OpenAI, Anthropic, Google, Together, Ollama). This enables consistent evaluation of prompt injection detection across different LLM backends and facilitates deployment in heterogeneous environments where multiple LLM providers are used.
Integrates with Purple Llama's LLM abstraction layer supporting OpenAI, Anthropic, Google, Together, and Ollama, enabling consistent prompt injection detection across heterogeneous LLM provider environments
Provider-agnostic detection versus provider-specific safeguards; enables multi-provider deployments but may not optimize for provider-specific vulnerabilities
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓LLM application developers building production systems with untrusted user inputs
- ✓Teams deploying multi-tenant LLM services requiring input validation
- ✓Security-conscious organizations implementing defense-in-depth for generative AI
- ✓International SaaS platforms serving multilingual user bases
- ✓Organizations with compliance requirements for non-English markets
- ✓Teams evaluating cross-lingual robustness of security measures
- ✓Enterprise teams building production LLM systems with multiple security layers
- ✓Organizations implementing defense-in-depth strategies across input/output/code execution
Known Limitations
- ⚠Binary classification only — returns true/false without confidence scores or attack type categorization
- ⚠Trained primarily on English prompt injection patterns; multilingual coverage may be limited
- ⚠Cannot detect novel zero-day injection techniques not represented in training data
- ⚠Requires integration into request pipeline; no built-in rate limiting or logging
- ⚠Multilingual coverage depends on machine translation quality; semantic drift in translation may reduce detection accuracy
- ⚠No explicit language identification — model processes all inputs with same weights regardless of language
Requirements
Input / Output
UnfragileRank
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About
Meta's classifier model for detecting prompt injection and jailbreak attempts in LLM inputs. Part of the Purple Llama project, it provides a lightweight binary classifier that can be deployed as a preprocessing filter for any LLM application.
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