Open Source Lakera Alternatives
Lakera is an AI-native security platform that protects LLM apps and agents from prompt injection, data leakage, and other generative AI threats in real time.
Lakera is an AI-native security platform built specifically to protect large language model applications, AI agents, and enterprise GenAI deployments from a new class of runtime threats. Rather than adapting traditional application security tooling, Lakera inspects prompts, model outputs, and agent actions in real time, detecting prompt injection attempts, jailbreaks, data exfiltration, and other adversarial behavior before they can affect production systems.
The platform is designed to sit in the request path with minimal overhead, offering sub-50ms runtime latency and support for over 100 languages so multilingual attacks can’t slip past detection. Its threat detection is context-aware and model-agnostic, working across conversational agents, document and RAG pipelines, and connected multi-agent systems, and it gives security teams a central policy layer to define and enforce rules by user, application, and action. Lakera also surfaces shadow AI usage across an organization’s applications and browsers, helping security teams see where GenAI is already running unmanaged.
Lakera is aimed at enterprises operating in regulated or high-stakes environments, including banking, financial services, and healthcare, alongside Fortune 500 companies and fast-moving AI startups who need to adopt generative AI without exposing themselves to novel prompt-based attack vectors.
What Lakera Offers
Real-time prompt attack detection
identifies prompt injection and jailbreak attempts as they happen, before a malicious prompt can influence model behavior
Data leakage protection
inspects model inputs and outputs to catch sensitive data exfiltration attempts across AI applications
Sub-50ms runtime latency
runs inline with production AI traffic without introducing noticeable delay to the user experience
Multilingual threat coverage
detects prompt-based attacks across 100+ languages, closing a common gap in English-only security tooling
Model-agnostic protection
works across different LLM providers and modalities rather than being tied to a single model vendor
Central policy control
lets security teams define and enforce AI usage policies by user, application, and specific action
Shadow AI discovery
surfaces unmanaged or unsanctioned GenAI usage across applications and browsers within an organization
Agent and RAG-aware detection
purpose-built to protect conversational agents, document/RAG pipelines, and connected multi-agent systems, not just single-turn chat
Common Use Cases
Securing customer-facing AI agents
product teams deploy Lakera in front of conversational agents to block prompt injection attempts from end users before they reach the model
Protecting RAG and document agents
teams running retrieval-augmented pipelines use Lakera to prevent injected instructions hidden in retrieved documents from hijacking agent behavior
GenAI gateway security
platform teams route all internal LLM traffic through Lakera to enforce consistent security policy across every AI application in the organization
Regulated industry compliance
security teams in banking and healthcare use Lakera's policy controls and audit visibility to meet compliance requirements for AI deployments
Shadow AI governance
CISOs use Lakera's discovery capabilities to find and assess AI tools employees are already using outside of sanctioned channels
