Building secure AI agents
Prompt injection isn't theoretical anymore. If you're building agents, attackers are already mapping your attack surface.
Why it matters
As AI agents move into production, prompt injection has become the critical security vulnerability that most teams aren't designing for. This isn't a future problem—it's a build-time decision that separates secure deployments from breached ones.
The key facts
10 to knowPrompt injection identified as most critical AI agent security risk
Unlike SQL injection, LLMs lack standard input isolation/escaping mechanisms
Agent architecture creates multiple attack vectors: user input, search results, retrieved documents can all override system prompts or trigger unintended tool calls
Published by Vercel (infrastructure provider perspective on AI safety)
Frames security as foundational design requirement, not afterthought
Prompt injection identified as most critical security risk for AI agents
No standard isolation or escape mechanism exists for LLM inputs (unlike SQL)
Attack surface includes user input, search results, and retrieved documents
Risk applies to any AI agent architecture (LLM + system prompt + tools)
Published by Vercel (infrastructure/deployment context)
Go to the source
Vercel Blogvercel.com
Publisher excerpt: An AI agent is a language model with a system prompt and a set of tools. Tools extend the model's capabilities by adding access to APIs, file systems, and external services. But they also create new paths for things to go wrong. The most critical security risk is . Similar to SQL injection, it…
