Prompt injection detection for LLM applications
Runtime detection and blocking system for indirect prompt injection attacks targeting deployed LLM applications, protecting against data exfiltration and model hijacking via crafted user inputs or poisoned third-party data.
The problem
LLM applications are vulnerable to prompt injection attacks where malicious inputs (direct or embedded in retrieved documents, web results, or user-generated content) manipulate model behavior to exfiltrate data, bypass guardrails, or execute unintended actions. Organizations deploying LLMs have no standard way to detect or block these attacks in production.
Who has it: Mid-market B2B SaaS and AI-native companies (50-500 employees) deploying LLM agents, RAG pipelines, or multi-turn chat systems handling sensitive data.
Why now: LLM application deployments are accelerating (RAG systems, agent frameworks, multi-step workflows) and security incidents from prompt injection are becoming public (Google Antigravity exfiltration, jailbreaks in deployed chatbots). Enterprises need runtime defense before incidents occur.
Where this came from
2 public sources behind this idea.
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