Lakera AI
LLM security platform that protects AI applications from prompt injection, jailbreaks, and sensitive data leakage - known for Gandalf, played by 1.5M+ users worldwide.
Lakera AI is a security platform that protects LLM-powered applications from prompt injection attacks, jailbreaks, and sensitive data leakage in real time. Founded in 2021 in Zurich by David Haber and a team of machine learning researchers, Lakera raised a $20M Series A in May 2024. The Lakera Guard API intercepts user inputs and model outputs, classifying them for injection attempts, harmful content, PII exposure, and policy violations before they reach or leave the LLM with latency under 100ms. Lakera is best known for Gandalf - a publicly available AI security challenge game played by over 1.5 million people that benchmarks LLM vulnerability detection and has become a widely cited demonstration of prompt injection risks. The API integrates with any LLM provider via a single middleware call.
Key Features
- Lakera Guard API intercepts LLM inputs and outputs to detect prompt injection, jailbreaks, and policy violations under 100ms
- PII detection identifies personal data exposure in user inputs before it is sent to the LLM or logged to application storage
- Harmful content moderation blocks hate speech, violence, and adult content generation in user-facing AI applications
- Single middleware integration works with any LLM provider including OpenAI, Anthropic, and self-hosted open-source models
- Gandalf security game provides a public benchmark for testing and demonstrating LLM prompt injection vulnerabilities
- Policy customization lets teams define application-specific restrictions beyond the built-in content moderation categories
- Real-time dashboard shows blocked threats, attack patterns, and policy violation trends across all integrated applications
Use Cases
- Developers adding prompt injection protection to customer-facing chatbots that accept arbitrary user input in production
- Enterprises ensuring that LLM-powered internal tools cannot be manipulated to leak confidential data or bypass access controls
- AI teams adding content moderation to text generation features without building a custom classifier or hiring trust and safety staff
- Security teams auditing LLM applications for injection vulnerabilities using Lakera Guard as a real-time detection layer
Pros
- Sub-100ms detection latency makes Guard practical as real-time middleware for user-facing applications at production scale
- $20M Series A and Zurich ML research team background provide confidence in the technical depth of the detection models
- Gandalf benchmark gives security teams a concrete, public demonstration of prompt injection risks for executive education
Cons
- Detection models trained on known attack patterns may miss novel or highly customized prompt injection techniques in practice
- Free tier API call limits may be insufficient for medium-scale applications before requiring a paid plan upgrade
- Middleware approach adds an external API dependency to every LLM call, creating a potential single point of failure if unavailable
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