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| Match Graph | 0 | 0 |
| Pricing | Free | Free |
| Capabilities | 15 decomposed | 8 decomposed |
| Times Matched | 0 | 0 |
Implements a modular scanner framework where both input (pre-LLM) and output (post-LLM) validators follow a common interface returning (sanitized_text, is_valid, risk_score) tuples. Scanners are composed independently and can be chained in arbitrary order, enabling flexible security pipelines. The architecture decouples scanner logic from orchestration, allowing developers to enable/disable scanners via configuration without code changes.
Unique: Unified scanner interface (scan() method returning triplet) across 36+ independent scanners (15 input, 21 output) allows arbitrary composition without coupling; architecture prioritizes modularity and configuration-driven behavior over monolithic validation logic
vs alternatives: More granular and composable than monolithic content filters; unlike generic ML-based content moderation APIs, LLM Guard provides specialized scanners for LLM-specific threats (prompt injection, token smuggling) with local execution and no external API dependencies
Detects prompt injection attacks using a multi-strategy approach combining regex-based pattern matching for known injection signatures, semantic similarity analysis against injection templates, and structural analysis of prompt delimiters and role-switching patterns. The scanner identifies attempts to override system instructions, inject new directives, or manipulate LLM behavior through adversarial prompt crafting.
Unique: Combines regex pattern matching for known injection signatures with semantic similarity scoring against injection templates and structural analysis of delimiter patterns; uses local embedding models rather than external APIs, enabling offline detection without cloud dependencies
vs alternatives: More specialized for LLM-specific injection vectors than generic input validation; faster than API-based detection services because it runs locally; more comprehensive than simple keyword filtering by combining multiple detection strategies
Supports ONNX (Open Neural Network Exchange) optimization for transformer-based scanners, enabling faster inference and reduced memory footprint. Converts HuggingFace models to ONNX format with quantization options (int8, float16), enabling deployment on CPU-only or edge devices. Configuration-driven ONNX enablement allows switching between full-precision and optimized models without code changes. Reduces model inference latency by 2-10x compared to PyTorch, enabling real-time scanning in latency-sensitive applications.
Unique: Provides configuration-driven ONNX optimization with quantization support (int8, float16) enabling 2-10x latency reduction; supports switching between full-precision and optimized models via configuration without code changes; enables deployment on CPU-only and edge devices where GPU acceleration is unavailable
vs alternatives: Faster inference than PyTorch models because ONNX Runtime is optimized for inference; more flexible than fixed-optimization approaches because quantization level is configurable; enables deployment scenarios (edge, serverless, CPU-only) that would be infeasible with full-precision models
Enables developers to compose scanners into custom security pipelines via configuration files (YAML) or code, selecting which scanners to enable, their order, and their parameters. Supports conditional scanner execution (e.g., run PII scanner only if prompt contains certain keywords), scanner chaining (output of one scanner feeds into next), and policy-driven behavior (different scanner sets for different user roles or risk profiles). Eliminates need to write custom orchestration code for complex security workflows.
Unique: Supports configuration-driven scanner composition via YAML or code, enabling policy-driven security pipelines without custom orchestration code; supports conditional scanner execution and chaining, enabling complex security workflows; enables different policies per deployment/user without code changes
vs alternatives: More flexible than hardcoded scanner sequences because policies are configuration-driven; more maintainable than custom orchestration code because logic is declarative; enables non-developers to modify security policies via configuration files
Provides hooks for logging and monitoring all scanning decisions, enabling compliance auditing and security analysis. Integrates with standard Python logging framework and supports custom observability backends. Logs include scanner name, input text, risk score, sanitization actions, and decision (allow/block). Enables teams to audit security decisions, identify patterns in attacks, and monitor scanner performance. Supports structured logging (JSON) for integration with log aggregation systems (ELK, Datadog, Splunk).
Unique: Integrates with Python logging framework enabling flexible log destination configuration; supports structured logging (JSON) for log aggregation systems; provides detailed audit trail of all scanning decisions including risk scores and sanitization actions
vs alternatives: More flexible than hardcoded logging because it integrates with Python logging framework; more comprehensive than simple decision logging because it includes risk scores and scanner details; enables compliance auditing and attack pattern analysis
Supports scanning multiple prompts or outputs in a single API call, enabling efficient batch processing for high-throughput scenarios. Processes batches through the scanner pipeline with optimized tensor operations and optional parallelization, reducing per-item overhead compared to individual requests.
Unique: Supports batch processing of multiple texts through the scanner pipeline with optimized tensor operations, reducing per-item overhead compared to individual scans. Enables efficient processing of large datasets without requiring separate API calls per text.
vs alternatives: More efficient than individual scans because it amortizes model loading and tokenization overhead across multiple texts; more flexible than fixed batch sizes because batch size is configurable.
Aggregates risk scores from multiple scanners using configurable strategies (weighted sum, maximum, AND/OR logic) to produce a final security decision. Enables policy-based rules (e.g., 'block if any scanner scores > 0.8 OR toxicity > 0.9') for nuanced security decisions beyond binary allow/block.
Unique: Provides configurable risk score aggregation with policy-based decision rules, enabling organizations to define nuanced security policies that weight different threats differently. Supports multiple aggregation strategies (weighted sum, maximum, AND/OR logic) for flexible policy expression.
vs alternatives: More flexible than binary scanners because it enables nuanced decisions based on risk scores; more maintainable than hardcoded logic because policies are declarative and configurable.
Detects personally identifiable information (names, emails, phone numbers, SSNs, credit cards, etc.) in prompts and outputs using pattern matching and NER (Named Entity Recognition) models. Detected PII can be anonymized by replacing with tokens and storing original values in a stateful Vault object, enabling later de-anonymization. The Vault class maintains in-memory or persistent storage of PII mappings, supporting workflows where sensitive data must be redacted from LLM context but recovered in responses.
Unique: Integrates stateful Vault class for PII storage and recovery, enabling reversible anonymization workflows; combines regex pattern matching for structured PII (SSN, credit card) with NER models for unstructured PII (names, organizations), supporting both detection and remediation in a single component
vs alternatives: More comprehensive than simple regex-based PII detection because it includes NER for context-aware entity recognition; unlike external PII masking services, runs locally with no API calls, enabling offline operation and compliance with data residency requirements; Vault system enables de-anonymization, supporting workflows where original values must be recovered
+7 more capabilities
Evaluates LLM outputs against curated question sets spanning three distinct hazard domains (biosecurity, cybersecurity, chemical security) using domain-expert-validated benchmarks. The assessment framework maps model responses to risk levels within each domain, enabling quantitative measurement of dangerous capability presence. Responses are scored against rubrics developed by security domain experts to identify whether models can produce actionable harmful information.
Unique: Combines expert-validated questions across three distinct security domains (biosecurity, cybersecurity, chemical) into a unified benchmark framework, rather than treating each domain separately. Uses domain-expert rubrics for scoring rather than automated classifiers, ensuring nuanced assessment of harmful capability presence.
vs alternatives: More comprehensive than single-domain safety benchmarks (e.g., ToxiGen for toxicity) because it measures dangerous knowledge across multiple hazard categories simultaneously, enabling holistic safety evaluation.
Provides standardized evaluation infrastructure to measure the effectiveness of unlearning techniques (methods that remove dangerous capabilities from trained models) by comparing model performance before and after unlearning interventions. The framework isolates the impact of unlearning by holding the benchmark constant while varying the model state, enabling quantitative assessment of whether dangerous knowledge has been successfully suppressed.
Unique: Provides a standardized evaluation harness specifically designed for unlearning research, with built-in comparison logic and side-effect detection. Unlike generic benchmarks, it explicitly measures delta between model states and flags unintended capability loss.
vs alternatives: More rigorous than ad-hoc unlearning evaluation because it enforces consistent benchmark administration, statistical testing, and side-effect measurement across all methods being compared.
WMDP scores higher at 63/100 vs LLM Guard at 56/100. LLM Guard leads on quality, while WMDP is stronger on ecosystem.
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Implements a structured scoring framework where model responses to dangerous knowledge questions are evaluated against expert-developed rubrics that assess the degree of hazard (e.g., specificity, actionability, completeness of harmful information). Responses are scored on multi-point scales (typically 0-4 or 0-5) rather than binary pass/fail, capturing nuance in how dangerous a model's output actually is. Rubrics are domain-specific (biosecurity, cybersecurity, chemical) and developed by subject matter experts to ensure validity.
Unique: Uses domain-expert-developed multi-point rubrics rather than automated classifiers or binary labels, enabling nuanced assessment of dangerous knowledge severity. Rubrics are calibrated to distinguish between vague, incomplete, and highly actionable harmful information.
vs alternatives: More interpretable and defensible than black-box classifiers because rubric criteria are explicit and expert-validated; enables stakeholders to understand why a response received a particular score.
Analyzes patterns in how dangerous knowledge correlates across the three benchmark domains (biosecurity, cybersecurity, chemical security), identifying whether models that excel at suppressing one type of hazard tend to suppress others. The analysis uses statistical correlation and clustering techniques to reveal whether dangerous capabilities are independent or coupled in model behavior. This enables understanding of whether unlearning interventions have domain-specific or global effects.
Unique: Explicitly analyzes relationships between dangerous knowledge across domains rather than treating each domain independently. Enables discovery of whether hazards are coupled or independent in model behavior.
vs alternatives: Provides deeper insight than single-domain benchmarks by revealing how safety properties interact across different hazard categories, informing more effective unlearning strategies.
Manages the creation, validation, and versioning of benchmark questions and rubrics through a structured curation pipeline involving domain experts, adversarial testing, and iterative refinement. The pipeline ensures questions are sufficiently difficult to elicit dangerous knowledge without being unrealistic, and rubrics are calibrated through inter-rater agreement studies. Version control enables tracking of benchmark evolution and ensures reproducibility across research papers.
Unique: Implements a formal curation pipeline with expert validation and inter-rater agreement checks, rather than ad-hoc question collection. Versioning enables reproducible research and transparent tracking of benchmark evolution.
vs alternatives: More rigorous than informal benchmarks because it enforces expert review, inter-rater validation, and version control, reducing bias and enabling reproducible comparisons across papers.
Provides a unified interface for evaluating diverse LLM architectures (open-source models, API-based models, fine-tuned variants) by abstracting away implementation differences. The abstraction handles API calls (OpenAI, Anthropic, etc.), local inference (Hugging Face, Ollama), and custom model serving, enabling consistent benchmark administration across heterogeneous model types. This enables fair comparison between models with different deployment modalities.
Unique: Abstracts away differences between API-based, local, and custom-deployed models through a unified interface, enabling fair comparison without reimplementing benchmark logic for each model type.
vs alternatives: More flexible than model-specific benchmarks because it supports any LLM architecture without code changes, reducing friction for researchers evaluating new models.
Implements rigorous statistical testing to determine whether differences in dangerous knowledge scores between models or unlearning methods are statistically significant or due to random variation. Uses techniques like bootstrap confidence intervals, permutation tests, and effect size estimation to quantify uncertainty in benchmark results. This prevents overconfident claims about safety improvements that may not be robust.
Unique: Integrates formal statistical testing into the benchmark evaluation pipeline rather than relying on point estimates, ensuring claims about safety improvements are statistically justified.
vs alternatives: More rigorous than informal comparisons because it quantifies uncertainty and prevents overconfident claims about safety improvements that may not be robust to sampling variation.
Employs adversarial testing techniques to validate that benchmark questions reliably elicit dangerous knowledge and cannot be easily circumvented by prompt engineering. Red-teamers attempt to find questions that fail to elicit dangerous knowledge or rubric edge cases, and the benchmark is iteratively refined based on findings. This ensures the benchmark is robust to adversarial adaptation and captures genuine dangerous capabilities rather than surface-level patterns.
Unique: Incorporates formal red-teaming into the benchmark validation pipeline rather than assuming questions are robust, ensuring the benchmark remains effective against adversarial adaptation.
vs alternatives: More robust than static benchmarks because it actively searches for evasion techniques and iteratively refines questions, reducing the risk that models can circumvent the benchmark through prompt engineering.