LLM Application Security addresses the unique vulnerabilities introduced when large language models are integrated into production applications. This category encompasses prompt firewalls, output guardrails, content filtering, and runtime protection for any application that incorporates LLM capabilities — from customer-facing chatbots to internal copilots and autonomous agents.
The core challenge is straightforward but technically demanding: LLMs are fundamentally different from traditional software. They accept natural language input, generate non-deterministic output, and can be manipulated through carefully crafted prompts in ways that bypass conventional security controls. The OWASP Top 10 for LLM Applications has codified these risks — prompt injection, insecure output handling, training data poisoning, and excessive agency among them — providing a framework that this category's vendors are building against.
Companies like Prompt Security (acquired by SentinelOne), CalypsoAI, and Arthur AI's guardrails platform provide the runtime security layer that sits between users and LLM applications, filtering both inputs and outputs in real time. Lasso Security focuses specifically on securing enterprise GenAI interactions, while WhyLabs and Galileo provide the evaluation and monitoring infrastructure that detects when LLM applications begin producing unsafe, inaccurate, or policy-violating content.
As enterprises embed LLMs deeper into critical workflows — from code generation to customer communications to financial analysis — the consequences of LLM security failures become business-critical. The companies in this category are building the equivalent of web application firewalls (WAFs) for the AI era. The market is young but moving fast, with significant acquisition activity already reshaping the competitive landscape.