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

In the past two years, businesses have been trying to fit large language models (LLMs) into support, analytics, development, and internal automation like never before. Along with the increasing adoption of AI technology, another trend is gaining momentum — cybercriminals… In the past two years, businesses have been trying to fit large language models (LLMs) into support, analytics, development, and internal automation like never before. Along with the increasing adoption of AI technology, another trend is gaining momentum — cybercriminals are taking advantage of the disconnect between assumptions about LLMs and their actual characteristics.In 2025 and 2026, several independent sources have highlighted the same trend: Prompt injection remains one of the most impactful and widely demonstrated attack vectors against LLM systems. The OWASP LLM Top 10 (2025) lists prompt injection as LLM01, identifying it as the most critical category of LLM‑specific vulnerabilities, for the second consecutive edition. OWASP's ranking reflects the fact that LLMs still struggle to reliably separate instructions from data, making them susceptible to manipulation through crafted inputs.CrowdStrike's 2026 Global Threat Report — built on frontline intelligence across more than 280 tracked adversaries — documented that threat actors injected malicious prompts into legitimate generative AI tools at more…