How developers secure AI agent access to APIs

AI agents create API security risks because they select tools and arguments dynamically, often while processing untrusted content, so valid credentials alone cannot ensure that resulting requests are authorized. Prompt instructions are unreliable as security controls, with research indicating that externally enforced tool visibility and call verification are far more effective at preventing unauthorized actions. Reported incidents involving Supabase, Asana, Salesloft Drift, and Composio illustrate how excessive permissions, broken tenant isolation, stolen tokens, and exposed credential stores can lead to data disclosure or compromise without requiring model jailbreaks. The recommended defense-in-depth approach keeps credentials outside model context, uses narrowly scoped and preferably per-user credentials, lets providers enforce user permissions where possible, checks caller-, tenant-, tool-, and argument-specific policies before execution, and contains runtimes with restricted network access and selective approval requirements for high-impact operations. MCP can authenticate agents to servers but does not itself enforce authorization boundaries, so tool servers must independently protect tenant, record, and argument-level access. The text presents Nango as an execution-layer service that resolves credentials at runtime, constrains provider requests through validated tools, supports multiple authentication methods, and maintains provider-level audit logs.

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