Access Control in the Era of AI Agents

AI Agents are autonomous systems capable of executing complex tasks, making API calls, and maybe even modifying a production database on your behalf. This evolution introduces a new security challenge and attack vector as these AI agents can introduce a level of unpredictability that some access control systems are not ready to handle.

In this blog post, you’ll learn about the history of AI agents, the risks they introduce and how to prevent them with a focus on fine-grained access control.

A Brief History of AI Agents

In the last few years, LLMs have evolved to become the mainstream place for questions, and more recently, they have been increasingly used to call APIs on the user’s behalf, delegate work to other models, etc.

We went from simple chatbots to multi-agent collaboration in a matter of a few years, and they all cover different capabilities:

• Chatbot: can handle written questions and return text and usually operates in a browser communicating through an API endpoint, everything happens within the application.
• Assistants: Produce a structured JSON that triggers a specific tool, they operate with an LLM
• Agentic System: Chains several tools in a plan it composed on the fly. They operate by communicating the LLM with multiple other services
• Multi-Agentic System: Negotiates tasks with other agents, they operate on an agent-to-agent basis.

Access-Control Models Under Stress

Security teams usually reach for Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), Permission or Policy-Based Access Control (PBAC), or Relationship-Based Access Control (ReBAC). Each shines in well-structured enterprise software; each shows strain when pointed at AI systems.

• RBAC is familiar and easy to audit. Unfortunately, an agent’s “role” can change from moment to moment. A request that begins as read-only can evolve into a code-generation exercise that needs write rights. Constant role hopping either floods your logs or drives teams to grant one oversized role that covers every possible combination.

• ABAC/PBAC add context: user tier, branch, time of day, tenant ID. They work well when the agent runtime can surface reliable attributes. The catch is that modern LLM stacks often rely on ephemeral containers or serverless functions where ambient context vanishes with each invocation. Persisting trustworthy attributes across the chain demands extra engineering that many proof-of-concept projects skip.

• ReBAC models complex resource graphs elegantly, perfect for mapping “Alice may comment on Bob’s ticket if they share a project.”. When the agent makes dozens of micro-tool calls per prompt, those lookups must complete in tens of milliseconds or users will notice a lag so your ReBAC systems must be fast

How Authorization Mitigates Top AI Security Risks (OWASP LLM Top 10)

The OWASP Top 10 for Large Language Model Applications 2025 explains the most critical risks for AI software and how to mitigate them. Let’s dig into it with a focus on access control.

LLM01 Prompt Injection
Attackers craft prompts that overwrite system instructions and steer the model toward harmful outcomes. A way to mitigate it is by implementing Input validation, adversarial testing and prompt isolation. In this context, Fine-Grained Access Control could block any tool call that is not explicitly scoped on the agent's permissions, so even a successful injection cannot reach sensitive operations.

LLM02 Sensitive Information Disclosure
Large models sometimes expose secrets from training data or leak confidential context provided at runtime. Some of the ways to mitigate this is by encrypting secrets, removing them from prompts and adding output filtering. Implementing least-privilege scopes is also a good way to mitigate, for example let’s say you want to get data from patient records, your agent would simply lack permission to perform the request, so disclosure stops at the policy level.

LLM03 Supply-Chain Compromise
It is very common to use third-party models for agent implementations, which could be poisoned or have plugins introducing backdoors and bias. A way to mitigate this is to sign every artifact, verify checksums, and keep a software bill of materials. Assign each external component its own low-privilege identity.

LLM04 Data and Model Poisoning
Attackers could add malicious examples into training or retrieval stores to change the model behaviour. One good way to mitigate this is by running rigorous data provenance checks and trusted-source allow lists help. In the context of access control, attribute-based rules can mark unvetted data as “not-usable,” preventing the agent from loading it until reviewers clear it.

LLM05 Improper Output Handling
When you give a response, you need to make sure it is validated and sanitized before passing it downstream to the user. It is important to always treat model output as untrusted, apply sanitization, and replay the action through the same access-control filter a human request would face.

LLM06 Excessive Agency
This risk is the vulnerability that enables damaging actions to be performed in response to unexpected, ambiguous or manipulated outputs from an LLM, for example giving an agent broad autonomy could end in unintended deletions, commits and payments. A way to mitigate this is by starting with read-only scopes, requiring user approval and implementing authorization in downstream systems rather than relying on an LLM to decide if an action is allowed or not, for example by using fine-grained authorization.

LLM07 System Prompt Leakage
This risk refers to when the system prompts or instructions used to steer the behavior of the model can also contain sensitive information that was not intended to be discovered. Internal instructions may reveal credentials or allowed APIs. Store prompts server-side, rotate them often and scrub sensitive content, you can also make sure that security controls are enforced independently from the LLM and as OWASP says, “in cases where an agent is performing tasks, if those tasks require different levels of access, then multiple agents should be used, each configured with the least privileges needed to perform the desired tasks.”

LLM08 Vector and Embedding Weaknesses
Weaknesses in how vectors and embeddings are generated, stored, or retrieved can be exploited by malicious actions to inject harmful content, manipulate model outputs, or access sensitive information. A way to mitigate this is to implement fine-grained access controls and permission-aware vector and embedding stores, as well as data validation and source authentication.

LLM09 Misinformation
Misinformation occurs when LLMs produce false or misleading information that appears credible. This vulnerability can lead to security breaches, reputational damage, and legal liability. A way to mitigate with retrieval-augmented generation to retrieve verified information from trusted sources, fact-checking APIs, and human review for high-risk tasks. Access control adds a guardrail by, for example, demanding a second verified source before an important write or financial transaction proceeds.

LLM10 Unbounded Resource Consumption
Unlimited requests drain budgets or become denial-of-service vectors. To mitigate this risk, you can implement strong access controls, including role-based access control (RBAC) and the principle of least privilege, to limit unauthorized access to LLM model repositories and training environments. You could also implement watermarking to detect unauthorized use of the LLM output.

How Fine-Grained Authorization Controls AI Agents

1. Least-Privilege Identities
   Give every agent or plugin its own low-privilege credentials and start each session in read-only mode. Grant extra verbs, such as write invoice or delete file, only after an explicit, audited elevation step.

2. Policy Checks on Every Call and Result
   Route every tool invocation and any generated SQL, code, or HTTP request through an external authorization service. The LLM can propose an action, but the policy, not the model, decides whether it runs.

3. Label and Quarantine Data Sources
   Tag incoming documents, datasets, and embeddings with trust levels like unknown, partner, or verified. Access rules block agents from training on or retrieving content until reviewers promote its label.

4. Protect System Prompts and Internal Configs
   Store prompts on the server side, remove secrets, and rotate them often. Even if a prompt leaks it reveals little, because the agent’s credentials unlock only the narrow slice of resources it truly needs.

5. Guardrails for Impact and Scale
   Require deterministic or human confirmation for payments, refunds, and code deploys, and apply per-identity rate limits and cost budgets. These measures blunt both hallucinated high-risk actions and runaway resource use.

Conclusion

AI agents amplify productivity, but without the right access control rules, they also amplify risk. It’s important to treat every agent and plugin as its own low-privilege client, check every request with an external policy engine, and enforce strict boundaries around data and costly actions.

Learn more about how Fine-Grained Authorization works and how you can use it to secure your AI Agents for example by securing a RAG application with Auth0 FGA.