Lakera Guard

Analyzes user prompts and LLM inputs in real-time using a context-aware detection engine trained on the world's largest prompt injection dataset. Operates at sub-50ms latency by processing prompts through a specialized neural classifier that understands syntactic attack patterns (e.g., instruction overrides, delimiter escapes, role-play jailbreaks) while maintaining semantic context from the surrounding conversation. Returns binary classification (safe/unsafe) with confidence scores and attack type categorization.

Detects and classifies jailbreak attempts—prompts designed to override system instructions, bypass safety guidelines, or manipulate LLM behavior through role-play, hypothetical scenarios, or authority manipulation. Uses a specialized classifier trained on jailbreak patterns (e.g., 'pretend you are an unrestricted AI', 'ignore previous instructions', 'act as DAN') and returns attack type labels (role-play jailbreak, instruction override, authority manipulation, etc.) with confidence scores. Integrates into request pipeline to block or flag suspicious inputs before LLM processing.

Analyzes threats relative to surrounding conversation context, system instructions, and user role rather than in isolation. Understands that the same prompt may be benign in one context (e.g., discussing security vulnerabilities in a security training chat) but malicious in another (e.g., attempting to override system instructions in a customer service bot). Uses conversation history, system prompts, and user metadata to reduce false positives and improve detection accuracy. Enables context-aware jailbreak detection that understands multi-turn manipulation and instruction override attempts.

Scans user prompts and LLM outputs for exposure of sensitive personally identifiable information (PII) such as email addresses, phone numbers, credit card numbers, social security numbers, and other regulated data. Uses pattern matching combined with context-aware classification to distinguish between legitimate references (e.g., 'email me at...') and accidental leakage. Operates in real-time with sub-50ms latency and supports 100+ languages for multilingual PII detection (e.g., Portuguese and Spanish banking data formats).

Detects and classifies toxic, abusive, hateful, or otherwise harmful language in user prompts and LLM outputs using a trained classifier. Analyzes text for profanity, hate speech, threats, harassment, and other harmful content categories. Operates in real-time with sub-50ms latency and supports 100+ languages. Returns binary classification (toxic/non-toxic) with content category labels and confidence scores, enabling applications to block, flag, or quarantine harmful inputs before LLM processing.

Provides a single, unified API endpoint for detecting multiple threat types (prompt injection, jailbreaks, PII leakage, toxic content) across any LLM application, regardless of which underlying LLM model is used (OpenAI, Anthropic, open-source models, etc.). Operates as a middleware layer that intercepts requests before LLM inference and responses after generation, enabling consistent security posture across heterogeneous model deployments. Abstracts threat detection logic from model-specific implementations, allowing teams to swap LLM providers without reconfiguring security rules.

Executes threat detection (prompt injection, jailbreaks, PII, toxic content) with sub-50ms latency, enabling integration into real-time LLM inference pipelines without significant performance degradation. Achieves low latency through optimized neural classifiers, efficient tokenization, and cloud-native deployment with geographic distribution. Designed for production deployments handling hundreds of prompts per second with minimal added latency to user-facing LLM applications.

Automatically scales threat detection capacity from zero to hundreds of prompts per second using cloud-native infrastructure and elastic resource allocation. Handles traffic spikes and variable load without manual scaling configuration or capacity planning. Designed for production deployments where threat detection must keep pace with LLM inference throughput without becoming a bottleneck. Manages concurrent requests, queuing, and resource allocation transparently to the client.

Detects prompt injection, jailbreaks, PII leakage, and toxic content across 100+ languages with language-specific pattern matching and context-aware classification. Supports regional data formats and cultural context (e.g., Portuguese and Spanish banking identifiers, multilingual PII patterns). Automatically detects input language or accepts explicit language specification. Enables consistent threat detection in global applications serving diverse linguistic user bases.

Optimizes threat detection to achieve a claimed 0.01% production false positive rate through context-aware classification and confidence scoring. Reduces unnecessary blocking of legitimate user inputs while maintaining high true positive detection rate. Enables production deployments where false positives directly impact user experience and application usability. Provides confidence scores and severity levels to allow applications to implement tiered responses (block, flag, warn) rather than binary accept/reject.

Assigns severity levels and risk scores to detected threats, enabling applications to implement tiered responses and prioritize security actions. Quantifies threat risk on a continuous scale (e.g., 0-1 confidence, low/medium/high severity) rather than binary safe/unsafe classification. Allows applications to block high-severity threats, flag medium-severity for review, and allow low-severity with warnings. Supports risk-based decision making in security workflows and incident response.