Lakera

Definition

Lakera is a commercial AI security platform that protects large language model applications against threats such as prompt injection, jailbreaking, data leakage, and insecure output handling. It provides a real-time detection layer that application developers can call to screen inputs and outputs before they reach or leave an LLM, trained on a large, continuously updated corpus of real-world adversarial attack data.

Overview

As organizations deploy LLM-powered chatbots and agents that accept untrusted user input and sometimes take actions or call tools, a distinct class of security risk has emerged around manipulating model behavior through crafted prompts. Lakera focuses specifically on this problem space, offering an API-based guard that inspects incoming prompts and outgoing model responses for signs of prompt injection, jailbreak attempts, and sensitive data exposure. The core product, often integrated as a middleware step in an application's request pipeline, returns a risk assessment for a given input or output that the calling application can use to block, flag, or log the interaction. Lakera builds its detection models using a large, continuously updated corpus of attack patterns, drawing in part from Gandalf, a public game the company built where users try to trick an AI into revealing a secret password, which has generated a substantial dataset of real-world jailbreak attempts. Beyond prompt injection detection, Lakera's platform addresses related concerns such as detecting personally identifiable information in prompts or completions, flagging toxic or unsafe content, and identifying attempts to exfiltrate a system prompt. This positions it as a security layer that complements, rather than replaces, an LLM provider's own built-in safety training and moderation endpoints. Lakera is aimed at teams building customer-facing or agentic LLM applications where the cost of a successful prompt injection, such as leaking confidential instructions or causing an agent to take an unintended action, is high enough to justify a dedicated security layer. Smaller or lower-risk applications may rely on provider-level safety features alone rather than adding a third-party security product. As a paid platform, Lakera requires integration into the application's request and response handling, and, like other detection-based security tools, its coverage evolves as new attack techniques emerge, meaning it is one layer of defense rather than a guarantee against all prompt-based attacks. Because detection-based security layers are trained on observed attack patterns, their effectiveness against genuinely novel injection techniques lags behind their effectiveness against known and closely related ones, which is why Lakera and similar vendors emphasize continual updates to their models rather than treating any single release as a finished defense. Teams integrating Lakera need to decide where in the pipeline to place the check, since screening only the initial user message misses injections embedded in retrieved documents or tool outputs that later enter the model's context, a scenario increasingly relevant for RAG and agentic applications. Cost and latency also factor into adoption decisions, since every guarded request incurs an additional network call and a small amount of added response time, which teams weigh against the severity of the risk a given application actually faces before deciding how many of the available checks to enable.

Key Features

• Real-time prompt injection and jailbreak detection API
• Detection of sensitive data and PII in prompts and completions
• System prompt leak and exfiltration attempt detection
• Continuously updated attack pattern models informed by real adversarial data
• Toxic and unsafe content flagging for inputs and outputs
• Middleware-style integration into existing LLM application pipelines
• Focus specifically on LLM and agent security rather than general app security

Use Cases

Alternatives

Frequently Asked Questions

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