How to Sandbox LLM Tool Calls to Prevent Security Exploits in Agentic AI

As AI agents gain the ability to run code, query databases, and browse the web, they create new attack surfaces that developers must actively secure before deploying to production. Classic web vulnerabilities such as prompt injection, path traversal, and server-side request forgery (SSRF) can be exploited through unchecked tool calls made by large language models. A key defensive measure is implementing a strict allowlist registry that only permits predefined, schema-validated tool calls, blocking any tool names or parameters the model attempts to invent. For agents that execute code, sandboxing via child processes with resource limits is recommended over insecure patterns like shell=True, which can expose systems to command injection. The guidance emphasizes layered controls — combining registry-level validation with handler-level checks — to minimize damage if an agent is compromised.