Large Language Models (LLMs) have moved well beyond simple text generation. Today, LLMs sit directly inside business operations, pulling data, updating records, managing files, running scripts, sending messages, and triggering automated workflows through plugins and integrated tools. This level of access improves efficiency but introduces risk from what the model is allowed to do.
OWASP Top 10 LLM 2025 addresses this concern in LLM06: Excessive Agency. The challenge is that organizations often grant LLMs more authority and system access than they need. These design decisions can expose powerful actions without the usual safeguards applied to systems that affect real-world data and processes, creating opportunities for unintended or unsafe changes to occur.
What Is an Excessive Agency?
Excessive Agency refers to a situation where an LLM is given more power, permissions, or system-level capabilities than the task truly requires. Instead of just generating text, the model can perform real actions such as updating records, sending messages, triggering workflows, or modifying files because the surrounding system grants that level of authority.
Once an LLM is connected to external systems, the model moves beyond generating responses and starts performing real actions. Through integrations, an LLM can call APIs, access databases, manage files, trigger automated workflows, or send messages. These actions are driven entirely by how the model interprets natural-language instructions, effectively allowing text inputs to translate directly into system behavior.
Risk arises when unclear or manipulated inputs cause the model to execute unintended actions. A crafted message, an embedded command within an uploaded file, or even a poorly worded request can push an LLM to perform high-impact operations.
How Does Excessive Agency Manifest Systems?
Excessive Agency becomes visible when LLMs gain more power, access, or autonomy.
Unrestrained Operational Capabilities
Unrestrained operational capabilities are a problem when LLM integrations provide far more functionality than required. Take a document reader, for example. The intended role should be limited to displaying content. Yet in many real-world implementations, the component is also granted the ability to delete files, rename documents, or export data.
Risk increases further when unused development plugins or broad; multi-purpose tools remain active in production environments. These integrations expose additional actions that the LLM can still call, even when those capabilities serve no business purpose. The real risk comes from unnecessary power that can be triggered by unpredictable model behavior, vague instructions, or manipulated prompts.
Overreaching Access Permissions
Excessive Agency also emerges when LLM-connected tools operate with more privilege than required. A simple utility built to retrieve customer information may be connected through a highly privileged account, granting the ability to modify or delete data that was never intended to be controlled.
In many real-world environments, extensions end up running under shared administrator identities because it feels convenient or easier to manage. But this choice shifts real risk into the system. The vulnerability lies in how access rights are assigned. With excessive permissions in place, even an ordinary output can trigger actions that have serious consequences, simply because the surrounding system allows those actions to go through.
Autonomous Execution Without Oversight
Another major contributor is unchecked automation. Many LLM-based systems execute tasks the moment an instruction is generated, without any form of human review. This creates risk when actions affect sensitive or irreversible processes, such as updating customer records, deploying cloud resources, sending external communications, or publishing content. In these moments, a misunderstood request, vague instruction, or manipulated input can lead directly to harmful outcomes.
Autonomy improves speed, but executing actions without validation or approval creates risk. Without proper safeguards, a single flawed instruction can quickly translate into a real-world action that is difficult or impossible to undo.
The Risks of Excessive LLM Actions on System Integrity
As LLMs become embedded in backend operations, the effects of unrestrained actions can expand across entire business processes. A model with write access could unintentionally change transaction records, billing data, or configuration settings. It might trigger automated workflows that cause financial losses, initiate mass communications, spin up cloud resources, or disrupt active services. In SaaS environments, an LLM could even set off administrative processes that affect platform-wide data or customer accounts.
When LLMs operate across connected channels, such as email platforms, messaging tools, APIs, or multi-agent systems, a single unexpected instruction can cascade through the environment and magnify the impact. What begins as a simple misinterpretation may escalate into large-scale operational disruption. Excessive Agency turns automation into a systemic risk.
OWASP LLM06:2025 – How to Mitigate Excessive Agency Risks
Preventing Excessive Agency requires controlling which extension capabilities are exposed to the LLM. Applications must be built in a way that prevents harmful actions even if the LLM model misinterprets or mishandles a request.
Limit Extensions to Single-Purpose Operations
Tools connected to an LLM should only perform the exact action they were created for, without offering unnecessary functionality. A document reader should remain strictly read-only, and unused development plugins must not remain active in production. Generic, open-ended tools such as shell command runners or broad URL fetchers introduce unnecessary risk and should be replaced with narrowly scoped, purpose-specific operations. Reducing available functions shrinks the attack surface and prevents the model from executing tasks outside its intended scope.
Enforce Privileges Outside the Model
Authorization must be controlled by downstream systems rather than the model’s output. Extensions should run using least-privilege identities tied to each user’s context, ensuring actions are executed only with the rights required for that specific user. Privileged service accounts should never be used for general model operations. When access rules are enforced externally and all requests are validated through complete mediation, any harmful instruction from the model becomes technically impossible to execute.
Keep Humans in Control of Critical Functions
Certain actions are too sensitive to be executed automatically. Operations such as updating records, publishing content, sending messages, or initiating cloud workflows should always require explicit user approval. The LLM may draft or recommend actions, but final execution must depend on human confirmation. This transforms model-driven systems from autonomous actors into supervised assistants, ensuring irreversible decisions remain under human control.
Validate Prompts and Outputs Before Execution
Anything an LLM reads or generates must be treated as untrusted input. User prompts, uploaded content, and even the model’s outputs need to be sanitized before being passed into application logic or downstream services. Adhering to standards such as OWASP ASVS and testing with tools like SAST, DAST, and IAST helps ensure that model-generated instructions cannot act as executable commands. With consistent validation, manipulated or malformed prompts are filtered out before they can have any operational impact.
Detect and Contain Unsafe Behavior
Even with preventive controls, unexpected actions may still be attempted. Monitoring should track unusual behavior, such as repeated API operations, abnormal data access, or rapid execution loops. Rate limiting helps slow potentially harmful activity, creating time to detect and respond before widespread damage occurs. By combining real-time observation with controlled execution, organizations can contain unintended behavior and reduce the impact of faulty or manipulated outputs.
