protectai/llm-guard

Features

• Prompt Injection Detectors - Detects and blocks prompt injection and jailbreak attempts to prevent malicious hijacking of model behavior.
• AI Content Filters - Scans and filters model inputs and outputs for toxicity, bias, and harmful content to ensure safety.
• LLM Prompt Injection Prevention - Scans and sanitizes user inputs to prevent prompt injections and adversarial attacks.
• Local Model Execution - Executes security scans using model files stored on local disk paths to remove external network dependencies.
• Local Model Orchestrators - Orchestrates the loading and execution of machine learning models from local disk to eliminate network dependencies.
• Model Output Scanning - Inspects generated responses to ensure they are safe and sanitized before delivery to the end user.
• Named Entity Recognition - Uses named entity recognition to identify and classify entities for the redaction of personally identifiable information.
• Topic Classifiers - Blocks model outputs that address predefined sensitive subjects using a zero-shot classifier.
• Text Toxicity Detection - Analyzes text for harmful or offensive language to block toxic content based on configurable thresholds.
• Bias Detection Guardrails - Analyzes responses for bias and sensitivity using guardrails to prevent data leakage and maintain safety.
• Sensitive Data Redaction - Replaces personally identifiable information or custom patterns in output to prevent data leakage.
• Sensitive Data Leakage - Scans responses for sensitive information and removes it to prevent accidental data exposure.
• Local Model Loading - Supports loading model weights from local directories to eliminate the need for downloading assets during startup.
• Streaming Output Modifiers - Analyzes generated text in real-time chunks to filter streaming responses before delivery.
• Content Moderation - Filters model responses for toxicity, bias, and hallucinations to ensure safe content delivery.
• Content Sanitization - Uses regular expression patterns to remove or modify undesirable text within model responses.
• Data Anonymization - Redacts personally identifiable information and sensitive data in prompts using NER and regular expressions.
• Data Leakage Detections - Inspects generated text and streaming chunks to detect and prevent the leakage of sensitive information.
• Adversarial Input Sanitization - Analyzes user input for malicious content and returns a sanitized version to ensure safe model interaction.
• LLM Safety Enforcers - Acts as an HTTP API that moderates interactions by enforcing security and compliance policies.
• LLM Governance and Compliance - Enforces organizational policies by blocking prohibited topics, competitor mentions, and unauthorized languages.
• LLM Guardrail Frameworks - Provides a set of scanners for validating the consistency, relevance, and safety of LLM content.
• LLM Input Security Scanning - Analyzes user prompts for security risks to prevent attacks or invalid inputs before they reach the model.
• PII Data Leakage Prevention - Identifies and redacts sensitive personal information in prompts and responses.
• Request Guards - Acts as a security guard that validates requests and blocks unsafe prompts before they reach the AI model.
• Secret Detection - Scans inputs for sensitive keys and tokens and replaces them with redactions to prevent data leakage.
• LLM Security - Functions as a security layer that scans inputs and outputs to block injections and toxicity.
• RAG Pipeline Scanning - Scans retrieved documents in RAG workflows for sensitive information and indirect prompt injections.
• Security Scanning Pipelines - Employs a modular scanner pipeline to process text through a sequence of independent security checks.
• AI Output Validation - Screens AI-generated content for bias, hallucinations, and malicious URLs to ensure safety and consistency.
• Agent Input Defenses - Defends autonomous agents from prompt injections to prevent unauthorized data access or harmful interactions.
• Model Output Verifications - Checks for factual consistency and banned topics in model responses to ensure reliable and safe outputs.
• Output Consistency Verifications - Verifies that model responses do not contradict the provided prompt to ensure logical consistency.
• Output Guardrails - Identifies and flags gibberish or incoherent text using output guardrails to ensure outputs remain intelligible.
• Sentiment Analysis Tools - Evaluates the emotional tone of generated text to flag content exceeding intensity thresholds.
• Sensitive Word Filters - Provides filters that detect and replace banned or sensitive terms in user prompts with redaction markers.
• Zero-Shot Classification Models - Uses pre-trained zero-shot classification models to categorize text into sensitive topics without task-specific training.
• Regex Pattern Matching - Applies regular expression rules to sanitize text and identify prohibited substrings or specific data formats.
• Real-Time Text Streaming - Inspects generated text in small sequential fragments to provide real-time security filtering for streaming responses.
• Input Token Estimators - Verifies input prompt length against token thresholds to prevent resource exhaustion and denial-of-service attacks.
• Pipeline Execution Controls - Implements a fail-fast model that terminates the security pipeline immediately after the first violation to reduce latency.
• Programming Language Detectors - Identifies programming languages within model outputs to permit or block specific languages.
• AI Agent Firewalls - Protects autonomous agents from malicious input manipulations to prevent unauthorized tool execution.
• Code Execution Prevention - Removes programming code from user inputs to prevent the execution of potentially harmful scripts.
• Competitor Filtering - Identifies and handles references to rival businesses using a customizable list of names to prevent inadvertent promotion.
• Custom Security Scan Extensions - Provides a pluggable architecture for defining custom scanning methods to identify specific security risks.
• Denial of Service Prevention - Limits the number of tokens in a request to prevent resource exhaustion and ensure system availability.
• Topic-Based AI Restrictions - Restricts AI interactions to specific permitted topics using zero-shot classification to block sensitive subjects.
• Input Sanitization - Filters input text using regular expression patterns to block prohibited content or enforce specific formats.
• Malicious Site Blockers - Analyzes links within generated text for phishing or harmful content to prevent users from visiting dangerous sites.
• Retrieval-Augmented Generation Security - Scans retrieved documents and generated answers in RAG workflows for security risks and sensitive data.
• Content Moderation Filters - Filters inputs and outputs across multiple providers through a unified proxy to maintain security standards.
• Named Entity Recognition Redactions - Uses named entity recognition and regular expressions to anonymize sensitive information.
• Pipeline Execution Optimizations - Reduces latency and resource load via fail-fast exits, asynchronous processing, and request sampling.
• Input Token Limiting - Validates input length against token thresholds to prevent resource exhaustion and denial-of-service attacks.
• Invisible Character Sanitization - Identifies and sanitizes hidden unicode tag characters used to bypass security filters.
• Guardrails and AI Safety - Listed in the “Guardrails and AI Safety” section of the The Incredible Pytorch awesome list.
• Red Teaming and Security - Security toolkit for validating LLM input and output.
• Safety and Security - Security toolkit for sanitizing and monitoring model interactions.
• Security & Privacy - Comprehensive security toolkit for anonymizing and sanitizing LLM inputs/outputs.

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