Agentic AI has been one of the hottest buzzwords of 2025, with developers and business owners racing to unlock the vast potential of AI. Agentic AI is a vital link in this technological chain, as it allows AI systems to make decisions and implement actions with little to no human input necessary.

If you have any experience with automation, that last sentence should fill you with a slight shiver of dread. Plenty of folks remember creating automated newsletters or social media feeds around a particular subject, only to later be horrified when they noticed unwanted content getting wrapped up in their aggregator. Automation can be amazing but it can also be risky. Without it, we run the risk of simply creating more work for ourselves.

With that in mind, we’ve put together ten risks of using agentic AI to help you know what to avoid. When possible, we’ve included some solutions as well. Cutting off problems before they occur will let you make the most of agentic AI without losing sleep at night.

1. Indeterministic, Unpredictable Behavior on Sensitive Endpoints

When humans interact with APIs, behavior tends to be predictable. Calls follow predictable flows, parameters are well-known and established, and side effects are kept to a minimum. AI agents, especially those driven by large language models or multi-agent orchestration, are fundamentally non-deterministic. Their sequence of API calls may vary drastically depending on the prompt or the model’s internal reasoning paths.

This unpredictability is particularly dangerous when agents access sensitive endpoints like data retrieval, user records, or financial transactions. An innocuous prompt could trigger unintended calls, expose data, or initiate privileged workflows. According to the latest Postman 2025 State of the API Report, AI agents making unauthorized or excessive API calls is the top security concern.

Mitigation involves adopting just-in-time credentialing, where agents receive temporary, narrowly scoped tokens only when needed. Treating each agent as a distinct identity with its own access token rather than reusing long-lived service keys can prevent unexpected escalations. Platforms such as Curity Identity Server support these approaches, allowing developers to issue ephemeral tokens and enforce scoped permissions.

2. Bursty, Hard-to-Manage Traffic

AI agents operate at machine speed and may issue hundreds or thousands of requests per second, especially in complex orchestration tasks. Traditional rate-limiting schemes tuned for human traffic are often not adequate, and bursty traffic can overwhelm backends, databases, or third-party services, leading to degraded performance or denial-of-service conditions.

Implementing dynamic rate-limiting and behavioral analytics is one of the most common and widely recommended ways to prepare for unexpected bursts of agentic traffic. Gateways or API management layers should enforce adaptive throttling, detect traffic spikes, and sandbox agent calls to prevent infrastructure strain. Postman emphasizes that agent-aware APIs must be monitored with traffic patterns in mind.

3. Data Overexposure Due to Broken Access Control

AI agents can access endpoints that developers never intended, aggregating sensitive data over multiple calls. Once aggregated, agents may output or leak data in ways that are difficult to monitor. According to the Postman report, nearly 49% of developers flagged this as a major issue.

Some ways to get around this include limiting access via least-privilege, scope-based access control, and treating each agent as a distinct identity. Identity experts recommend defining fine-grained permissions, embedding identity information in access tokens, and enforcing policy-based authorization at runtime to limit data exposure.

4. Privilege Escalation Due to Lack of Authorization Checks (BOLA)

AI agents can exploit under-secured endpoints, escalate privileges, and perform unauthorized operations — a classic Broken Object-Level Access (BOLA) scenario. This risk becomes more pronounced when agents have dynamic lifecycles or can spawn sub-agents. Research shows that state-of-the-art agents often violate policies within just a few queries.

Employing fine-grained identity and capability-based access control helps to keep privilege escalation to a minimum. So do emerging protocols like Agentic JWT, which bind API calls to verifiable intent, limiting scope and preventing unauthorized escalation.

5. Credential Leakage and Blast Radius of Compromised Agents

Static, long-lived API keys shared across agents represent a significant security risk. If an API key leaks, an attacker gains access to all associated APIs. The potential impact of compromised credentials can be enormous. Postman reports that 46% of developers are concerned about credential leaks.

Issuing ephemeral, short-lived tokens is an important step toward reducing the risk of credentials being leaked, as are rotating credentials frequently, avoiding embedding static keys, and storing credentials in secure vaults. A zero-trust approach, where each API call verifies identity and intent, further reduces the risk of credential misuse.

6. Shadow Agents and Lifecycle Issues

Unmanaged or orphaned agents that still possess valid tokens are another security risk. Sometimes, agents persist beyond their intended purpose, creating shadow AI that’s difficult to monitor or secure. These agents are still able to make calls, potentially violating policy or exposing data.

Implementing an agent-centric identity governance system helps to eliminate shadow agents. To do so, certain agentic platforms can inventory agents, retire orphaned identities, and enforce proper permissions throughout the agent lifecycle.

7. Tool-Squatting and Supply-Chain Risks

In multi-agent systems, agents are able to invoke external tools or plugins. Malicious actors can register or impersonate legitimate tools, causing unintended calls or data leaks. The risk is particularly high in environments where agents automatically discover and execute external services.

Using a centralized tool registry, enforcing fine-grained access policies, and applying dynamic credentialing reduce the risk of tool-squatting. A registry-based zero-trust architecture ensures that agents only access approved tools.

8. Prompt Injection and Unexpected Behaviors

Agents interpret natural-language prompts, which may then be used to generate API requests dynamically. Malicious or poorly crafted prompts can trick agents into exposing sensitive data or performing unauthorized actions. Prompt injection remains a subtle and persistent threat in AI systems.

Limiting the endpoints agents are able to call through scoped tokens helps limit the damage that malicious prompts are capable of, as does implementing runtime monitoring and anomaly detection, and using output filtering. Incorporating human-in-the-loop confirmation for critical workflows helps to make sure that actions are intentional, as well. Applying zero-trust principles guarantees each request is authenticated, authorized, and logged.

9. Compliance and Regulatory Risks

AI agents accessing sensitive data may inadvertently violate GDPR, HIPAA, or other regulations. High-volume, machine-generated calls make traceability challenging and increase the risk of non-compliance.

Enforcing ephemeral, scoped tokens reduces the risks of an agent violating regulations, as does logging every agent call with identity attribution, centralizing API gateways and identity management enforcement, and applying data minimization. Rate-limiting and least-privilege principles also help reduce regulatory exposure.

10. Over-Privileged Agents and Privilege Creep

Even minimal permissions have a way of expanding over time, resulting in over-privileged agents. When combined with stale credentials or shadow agents, this creates internal threats that can be exploited maliciously or unintentionally.

Just-in-time provisioning reduces the risk of over-privileged agents, as does periodic review of agent permissions, enforcing just-enough-access principles, and applying zero-trust with conditional access. Continuous monitoring ensures that agents maintain only the permissions necessary to perform their tasks.

Practical Recommendations to Reduce the Risk of Agentic AI

To safely manage agentic API access, organizations should treat agents as first-class identities. Issuing short-lived, just-in-time credentials, combined with central API gateways for rate-limiting and logging, helps prevent abuse. Applying least-privilege and just-enough-access principles is critical, as is enforcing zero trust — every request must be authenticated, authorized, and logged.

Other practical recommendations for securing agentic AI include:

• Monitoring agent behavior for spikes, anomalies, and abnormal data access ensures early detection of unexpected actions.
• Maintaining audit trails and conducting periodic privilege reviews reduces the risk of over-privileged or orphaned agents.
• For multi-agent ecosystems, using a tool registry can prevent tool-squatting, and human-in-the-loop confirmations provide an additional safeguard for sensitive workflows.

Key Takeaways for Securing Agentic AI

Agentic API access unlocks automation and scalability but dramatically changes the threat model. Risks like credential leakage, privilege escalation, bursty traffic, and unexpected agent behavior are real and significant.

Emerging standards, protocols, and agent-centric identity and access management solutions offer practical ways to help limit these risks. Implementing ephemeral tokens, least-privilege access, zero trust, and continuous monitoring allows AI agents to operate as powerful, productive, and safe members of an infrastructure.

For organizations that provide APIs or digital services, early adoption of these best practices is crucial to ensure that agentic access is a security asset rather than a liability.