Prompt injection is exploiting enterprise AI's biggest design flaws by targeting agents, RAG pipelines and model routers

Enterprises are rapidly embedding LLMs into support, analytics, software development, and automation, but attackers are exploiting the gap between how these systems are designed and how they behave. Reports from OWASP and CrowdStrike identify prompt injection as a top LLM security risk, with real incidents showing attackers using crafted inputs to steal credentials, cryptocurrency, and private data.

The threat is shifting from isolated chatbot abuse to attacks on the broader enterprise AI stack, including agents, retrieval pipelines, and automated workflows. As LLMs gain access to internal data and tools, AI security will need to focus less on model accuracy alone and more on permission boundaries, input isolation, and runtime monitoring.