Lakera Guard

Analyzes incoming prompts and user inputs in real-time to detect prompt injection attacks before they reach the LLM, using a neural model trained on the world's largest prompt injection dataset. The API processes requests synchronously with claimed sub-50ms latency, enabling inline deployment in production LLM pipelines without noticeable user-facing delay. Detection operates model-agnostically across any LLM backend (OpenAI, Anthropic, open-source, etc.) by analyzing prompt structure and semantic intent rather than model-specific artifacts.

Identifies and blocks jailbreak prompts—carefully crafted inputs designed to circumvent an LLM's safety guidelines—by analyzing prompt semantics, role-play framing, and instruction-override patterns. The detection model recognizes common jailbreak techniques (e.g., 'pretend you are an unrestricted AI', 'ignore your guidelines', hypothetical scenarios designed to elicit unsafe content) and flags them before the prompt reaches the LLM, preventing the LLM from being manipulated into generating harmful content.

Enables centralized threat policy management across multiple LLM applications and deployments, allowing security teams to define threat policies once and apply them consistently across all applications without per-application configuration. Policies can be updated globally without redeploying applications, enabling rapid response to emerging threats or policy changes. This provides a control plane for LLM security across an organization's entire LLM portfolio.

Provides bidirectional threat detection that scans both user inputs (before they reach the LLM) and LLM outputs (before they're returned to users). This dual-direction approach prevents both adversarial inputs (prompt injection, jailbreaks) and harmful outputs (toxic content, PII leakage from the LLM's training data). The API can be called at two points in the request/response pipeline: before LLM inference (to protect the LLM) and after LLM inference (to protect users).

Analyzes user inputs and LLM outputs for toxic, abusive, hateful, or otherwise harmful language across 100+ languages. The detection model identifies profanity, slurs, harassment, threats, and other content that violates community standards or platform policies. Operates in real-time with sub-50ms latency, allowing toxic content to be flagged, filtered, or logged before it reaches users or is stored in application logs.

Detects and flags the presence of sensitive personally identifiable information (PII) in user inputs and LLM outputs, including email addresses, phone numbers, credit card numbers, social security numbers, names, addresses, and other regulated data. The detection model uses pattern matching and semantic analysis to identify PII across multiple formats and languages, enabling applications to prevent accidental exposure of sensitive data in logs, outputs, or external integrations.

Provides unified threat detection (prompt injection, jailbreaks, toxic content, PII) that works identically across any LLM backend—OpenAI, Anthropic, open-source models, custom fine-tuned models, or multi-model ensembles. The detection operates at the input/output level rather than relying on model-specific safety mechanisms, enabling consistent security posture regardless of which LLM provider or version is used. This allows teams to switch LLM providers or use multiple models in parallel without reconfiguring security policies.

Provides threat detection via a synchronous REST API that integrates directly into request/response pipelines, enabling inline security checks without asynchronous processing or external queues. The API accepts a prompt or text input and returns threat detection results (injection, jailbreak, toxic, PII flags) within sub-50ms, allowing the application to make immediate allow/block decisions before passing data to the LLM or returning it to users. Integration is straightforward: call the API before LLM inference or after LLM output generation, and handle the response synchronously.

Detects prompt injection, jailbreaks, toxic content, and PII across 100+ languages using a single unified model rather than language-specific classifiers. The detection model is trained on multilingual data and can identify threats in any supported language without requiring language detection or separate language-specific pipelines. This enables global applications to apply consistent security policies across all user languages without managing multiple detection models or language-specific rules.

Designed for production deployment in high-volume LLM applications with claimed 0.01% false positive rate, meaning only 1 in 10,000 benign inputs are incorrectly flagged as threats. The detection model is optimized for precision (minimizing false positives that block legitimate users) while maintaining recall (catching actual threats). The API claims to 'scale effortlessly from zero to hundreds of prompts per second', suggesting horizontal scalability and load balancing for production traffic.

Detects threats with contextual awareness by analyzing not just the presence of suspicious patterns but the intent and context in which they appear. The detection model returns risk scores (numeric confidence levels) rather than binary flags, enabling applications to implement graduated responses (warn user, require confirmation, block) based on threat severity. This allows legitimate use cases (e.g., discussing security vulnerabilities, creative writing with mature themes) to proceed with warnings rather than being blocked outright.

Claims to adapt threat detection in real-time to emerging attack patterns without requiring manual model retraining or deployment of new model versions. The detection system observes new threat patterns in production traffic and incorporates them into detection logic automatically, enabling the system to defend against zero-day prompt injection techniques and novel jailbreak methods as they emerge. This contrasts with static models that require periodic retraining and deployment cycles.