SSO for AI Agents: Authentication and Authorization Guide

AI agents often need to access multiple services and data sources on behalf of users. This introduces unique authentication and authorization challenges that go beyond typical single sign-on (SSO) for human users. Unlike a standard web app, an AI agent might operate without a user interface and even make autonomous decisions. To keep these agents secure and effective, it's critical to use best practices like least-privilege access and just-in-time authentication, and to understand where traditional auth flows fall short.

This guide outlines how to handle SSO for AI agents in a secure way. We cover why you should minimize an agent's permissions, how to approach user authorization only when needed, and why the AI model itself must be kept out of sensitive security processes. We also highlight the limitations of current OAuth/SAML flows for agent use cases and explain why relying on browser automation for agent auth is not a robust solution. Finally, we show how new solutions (like Arcade.dev) can simplify authentication for AI agents while adhering to these security principles.

Least Privilege for AI Agents

AI agents should follow a strict least privilege model: only give them the minimum permissions necessary for each task. Granting an agent broad access "just in case" is dangerous. If the agent misinterprets instructions or is compromised, any extra privileges can cause serious damage. You can’t fully trust an AI to always make safe decisions, so it's vital to limit what it's allowed to do.

User OAuth scopes: When an agent authenticates as a user via OAuth, request only the scopes it truly needs (e.g. read-only access if it just needs to fetch data). Fine-grained OAuth scopes are ideal for this, allowing precise control over what the agent can do (AI agent identity: it's just OAuth). For instance, an email-sorting bot might get permission to read emails but not delete them.
Service account permissions: If the agent uses its own service account or API key, give that account tightly scoped permissions. Create dedicated roles for the agent that restrict actions to the bare minimum (for example, a cloud cleanup agent gets access only to specific storage buckets, not the entire cloud account).
LLM non-determinism: Because the agent’s underlying AI model can behave unpredictably, it’s risky to ever give it sweeping admin rights. For example, an AI file-management agent with unrestricted filesystem access once tried to delete the entire root directory by mistake – a catastrophic action that would have been impossible if it only had read-only permissions. This real incident underscores why even "intelligent" agents must be kept on a tight leash when it comes to privileges.

Just-in-Time Authentication (Best Practice)

Another best practice is to use just-in-time (JIT) authentication for AI agent integrations. Unlike a traditional app where you might connect all services upfront, an AI agent can dynamically request access as new needs arise. You should not force users to pre-authorize every possible integration during onboarding. Instead, the agent should prompt for authentication only when the user actually tries to use that service.

This on-demand approach keeps the user experience smooth and secure:

Minimal upfront friction: Users can start using the agent without a long setup of connecting accounts they might never use.
Timed with actual need: The agent asks for credentials or OAuth consent exactly when the user requests an action that requires it. For example, if the user first asks the agent to book a flight on Delta, the agent can then prompt them to log into their Delta account. (Similarly, it would only ask for a Marriott login when the user decides to book a Marriott hotel.)
Reduced credential exposure: By not storing tokens for unused services, you reduce the overall attack surface. If the user never uses the Hilton integration, no credentials or tokens for Hilton are ever obtained or stored.

In short, AI agents should expand their authenticated access just-in-time based on user requests, rather than assuming everything in advance. This way, users aren’t burdened with granting a bunch of permissions they may not need, and credentials are only handled when necessary.

LLMs Cannot Process Security Flows

It’s critical to isolate authentication flows from the AI model. Large language models (LLMs) should never handle login processes or be exposed to raw credentials. An AI agent’s "brain" might be smart with language, but it isn’t a secure actor and can easily mishandle sensitive info. Developers must design systems so that all credential handling and security steps occur outside the LLM.

Letting an LLM manage authentication leads to serious risks:

Accidental credential leaks: The model could inadvertently output a password or token in a response or log. (LLMs embedded in applications have been known to expose sensitive data through their outputs (LLM02:2025 Sensitive Information Disclosure - OWASP Top 10 for LLM & Generative AI Security).)
Misinterpreted auth steps: An LLM might not reliably follow a multi-step auth flow. For example, it could get confused by a two-factor authentication prompt or a web redirect step and fail to complete the login correctly.
Phishing vulnerability: The AI cannot truly understand if a login page is fake or malicious. It might hand over credentials to a phishing page because it doesn't have the judgment a human user does.
Unauthorized token reuse: If the model somehow gets hold of a session token or cookie, it might use it in unintended ways or expose it elsewhere, since it doesn’t inherently know which uses are allowed or safe.

In practice, this means any OAuth exchange, SAML response, API key, or user password should be handled by secure backend logic or a dedicated UI component – not by the LLM. The AI agent can be told afterwards that “authentication succeeded,” but the actual security exchange should happen in a controlled, deterministic way that the LLM can’t alter or see. This separation ensures that prompt injections or glitches in the AI won’t compromise credentials or the integrity of the auth flow.

Auth Flows Were Not Designed for AI Agents

Modern identity protocols like OAuth 2.0 and SAML were originally built with web and mobile applications in mind, not autonomous agents. These flows assume a human user is present to click a login button, grant consent, and complete multi-factor prompts. An AI agent often runs headless (no UI) or in a backend server environment, which makes the standard OAuth “redirect to browser for login” process awkward to implement. Developers often struggle to fit the square peg of OAuth into the round hole of an agent system.

Yet, to integrate with popular services, there's no way around it – services like Google (Gmail), Microsoft (Outlook), Slack, Salesforce, and others require OAuth or SSO for API access. The result is a key challenge for agent developers: how to initiate and complete these auth flows in a backend or CLI environment. Common workarounds include using OAuth device codes (where the agent provides a link/code for the user to authorize via a separate device) or pre-generating tokens for the agent, but these add friction and complexity.

The core problem is that identity flows assume a user-driven session. AI agents flip that model – the "user" (the agent) is a program acting on someone’s behalf. As one security expert put it, our current identity frameworks were never designed for autonomous agents, and “the gaps are starting to show.” (Agentic AI and Authentication: Exploring Some Unanswered Questions - Spherical Cow Consulting) Until standards evolve, developers must bridge this gap with custom solutions or tools that can handle OAuth on behalf of an agent.

Problems with Browser-Based Agents

A naive approach to agent authentication is to automate a web browser (or headless browser) to simulate a user logging in. This might work in simple cases, but it’s generally a weak and brittle strategy. Browser-based agents have little control over the authentication flow beyond what a normal user sees, and they run into numerous obstacles:

OAuth redirects & callbacks: An automated browser can load the login page, but capturing the resulting token from an OAuth redirect (the callback URL) is complicated without a real backend server to receive it. It’s not a clean solution for obtaining tokens.
Bot detection measures: Many services employ CAPTCHAs, bot-detection scripts, IP rate limiting, and other techniques to block automated logins. A headless browser agent will likely get flagged or blocked by these defenses.
Unreliable and hard to maintain: Web pages and workflows change frequently. A script that navigates a login form can break whenever the UI or flow updates. Relying on scraping a web interface for auth is fragile. It also often violates terms of service, and it's not scalable or secure for production use.

In short, AI agents need native API integrations, not hacky browser automation. Whenever possible, use official APIs and standard auth flows (OAuth, API keys, etc.) through code rather than trying to fake a web UI login. This is more robust and avoids the arms race of trying to dodge anti-bot measures. The goal is to have the agent interact with services as a legitimate API client, not as a scripted web browser.

Conclusion

Building SSO and auth for AI agents comes with many pitfalls, but there are solutions emerging to handle this complexity. Arcade.dev is one platform that addresses these challenges head-on.

In summary, Arcade provides:

Managed OAuth and security flows: Arcade handles the entire OAuth exchange and other auth steps for you, so you don’t have to write custom auth code from scratch for each service.
Just-in-time authentication: It allows agents to request user authorization only at the moment it’s needed, rather than requiring every integration to be pre-authorized upfront.
LLM-safe design: The AI model never sees or processes credentials when using Arcade. All sensitive tokens and secrets are kept away from the LLM, keeping authentication flows secure.
Support for non-web agents: Arcade makes it straightforward for a backend or headless AI agent to authenticate to services like Gmail, Slack, Salesforce, and others that expect OAuth. It bridges the gap for agents that don’t have a browser or traditional front-end.
API-native integrations (no scraping): Using Arcade means you don’t need to rely on brittle browser automation. The agent connects directly to service APIs with proper auth, so you avoid CAPTCHAs and anti-bot hurdles entirely.

By incorporating these practices and tools, you can securely empower your AI agents to act on behalf of users without compromising security or user experience. Learn more about how Arcade helps with SSO for AI Agents or sign up for a free account.