One of the most difficult aspects of building systems with multiple AI agents is managing permissions. Single AI agents often accumulate permissions as they are updated with expanded capabilities, enabling them to operate beyond their original purpose. The accumulation of permissions, referred to as privilege drift or scope sprawl, leads to broader access points and an expanded API attack surface with numerous potential risks.

A multi-agent system, where multiple autonomous AI agents work together to complete tasks or solve problems, amplifies this problem far beyond what a single agent faces. This amplification occurs because permissions multiply across agents, shared resources, and inter-agent communication.

Below, we’ll explore how privilege drift arises in agentic systems and review some practical tips for mitigating privilege drift within environments that involve multiple agents.

What Is Privilege Drift in Agentic Systems?

One of the top risks architects of agentic systems face is “privilege drift,” a problem that can occur with a single agent or multiple agents. Privilege drift is where permissions accumulate over time beyond what was originally granted and without reference to the original purpose the agent was designed to fulfill.

Within agentic systems, privilege drift can quickly lead to over-permissioned access, breaking the principle of least privilege. This can unintentionally result in exposing sensitive information or functions.

Privilege Drift: Single Agent

What can privilege drift look like for a single agent? Ken Huang, author and renowned expert in AI and Web3, provides an excellent example of privilege drift using a single “Quarterly Audit Agent.” At launch, the fictional agent has three permissions that seem to be scoped appropriately:

• Finance 2025 (Read Access)
• Cloud Costs (Read Access)
• Audit Summary (Write Access)

Over the course of three months, the development team extends the agent, adding one new capability per month:

• 1st Month: HR Compensation Costs (Read Access)
• 2nd Month: External Tax Filing API (Read Access)
• 3rd Month: Email Gateway API (Send)

The original intent of the agent was to automatically generate quarterly audit reports with minimal resources and permissions. However, it has now morphed into a headcount audit and tax compliance agent. It has the ability to exfiltrate sensitive information, such as personally identifiable information (PII), employee compensation, and tax records, via email.

Each individual extension of the audit agent seems appropriate by itself. However, the result is an agent that has evolved beyond its original purpose and doubled its permissions, significantly expanding its access surface. In certain scenarios, this privilege drift could lead to regulatory and compliance issues.

Privilege Drift: Multi-Agent System

The above example involves a single AI agent. Now imagine that this Quarterly Audit Agent is part of a FinOps Governance Platform with four other agents. Each agent follows the same structural arc — a tight initial scope that gets extended several times over three months for legitimate operational reasons.

FinOps Governance Platform: Privilege Drift With 5 Agents

In this FinOps Governance Platform hypothetical scenario, no single extension appears unreasonable. Each has been approved by the development team and makes sense in their own context. However, now the five agents powering the platform collectively have:

• 20 API read scopes
• 5 API write surfaces
• 1 outbound communication channel (via an API they all share)
• Access to many types of sensitive data, including:
  • Personal and employee data (PII)
  • Customer identity and AML data
  • Financial and accounting data
  • Regulatory filing data

For each agent in the platform, the original use case that justified its existence now represents about 20% of its actual activity. The remaining 80% is accumulated capability that the team did not plan, review holistically, or map against a threat model.

The Core Problem: Permissions Are Attack Vectors

The accumulation of permissions for each agent in a multi-agent system (MAS) creates an expanded attack surface with a number of potential risks.

Prompt Injection Attacks

A prompt injection attack is where malicious or misleading instructions are embedded in external content that a large language model (LLM) or AI agent (powered by an LLM) processes to manipulate its output.

All five agents could see attempts at prompt injection attacks. For example, an attacker could submit a contract with embedded text to the Vendor Risk Agent that is read through the Vendor Management API. The injection results in a legitimate vendor getting flagged as a critical risk and immediately suspended.

With more permissions added throughout the three-month period, attackers can use prompt injections for many more malicious and fraudulent activities.

Over-Privileged Service Accounts

A service account is a non-human identity built to interact with infrastructure, systems, APIs, or applications. Typically, these accounts are tied to a specific, narrow function and hold machine-readable credentials. Service accounts are typically over-privileged by default, receiving broad permissions at startup to ensure agents have the functionality needed to do the work they were designed for. These permissions are rarely reduced, and privilege drift exacerbates the problem.

In the FinOps Governance Platform example, each agent uses multiple service accounts where credentials are stored in .env files that were accidentally exposed in misconfigured cloud buckets. An attacker could discover the exposed credentials for the Banking API and the SWIFT Gateway API, which the Treasury and Liquidity Agent uses.

Due to privilege drift, the banking API has elevated permissions, allowing the attacker to initiate an unauthorized wire transfer that is then sent via the SWIFT Gateway API. These are only two of the risks that can stem from privilege drift. Other risks include lateral movement between agents and cascading authorization failures.

Managing Privilege Drift in Multi-Agent Systems

Privilege drift is an ongoing challenge for multi-agent system architects. However, there are steps that they can take to manage permissions in a system powered by AI agents. Below, we’ll review some of the top mitigations.

Maintain a Platform-Wide Permission Inventory

Track the permissions for every agent in your MAS, starting at the initial launch of each agent and continuing with each extension. This inventory can include:

• The current permissions for every service account and API available to each agent
• The date each permission was added
• The business justification for each permission
• Any shared resources among the agents and permissions overlap

In the FinOps Governance Platform scenario, the extension for each agent seemed reasonable in isolation. A platform-wide inventory makes the aggregate permissions visible.

Follow the Principle of Least Privilege (PoLP)

Make sure each agent is granted only the permissions needed to do the specific task it was built for. Do not give them broad admin or super admin permissions. Least privilege should be enforced at runtime at the point of tool invocation. Runtime enforcement of least privilege reduces the potential for widespread damage if the agent is compromised.

Implement Just-In-Time Access (JIT)

JIT access means permissions are blocked by default and only granted at the exact time access is needed. Once the agent completes the task, access is automatically revoked. For example, if the Treasury and Liquidity Agent must make a request to the Banking API, it is granted a short-lived, operation-specific token on demand rather than one that is long-lived and sits in an .env file. This limited window reduces the risk of unauthorized access to critical systems and sensitive data.

Monitor the Behavior of Every Agent

Static permission tracking only catches excessive permissions at review time. However, continuously monitoring each agent’s behavior helps teams detect when agents have permissions they shouldn’t or access to resources that go beyond their original intended purpose. For example, monitoring would catch an agent calling an API it has never called before in normal operations — an anomaly that warrants investigation. Applying these basic approaches to every agent in your MAS will help mitigate the risks that come with privilege drift.

Don’t Wait to Manage Permissions in Multi-Agent Systems

The more permissions your MAS has, the larger the access and API attack surface. While it may not be possible to completely prevent privilege drift, it is possible to manage permissions in a way that reduces the risks it poses. Preparing for privilege drift before launch, starting with the four approaches outlined above — permission inventory, least privilege, JIT access, and behavioral monitoring — will help you secure your MAS against potential attacks. So, no matter how large the attack surface becomes, you’ll have some mechanisms to meaningfully reduce your risk.