Agentic AI Risks: 12 Risks and 10 Best Practices

What Are Agentic AI Risks?

Agentic AI (autonomous systems that plan, reason, and take multi-step actions across external tools and databases) creates unique risks that go beyond traditional generative AI or static software scripts. Unlike traditional AI systems that primarily generate responses, agentic AI can interact with external tools, access sensitive data, execute code, and modify business processes. This autonomy increases the impact of errors and makes agent behavior harder to predict and control.

Security risks and threats associated with agentic AI include:

• Prompt injection and agent manipulation: Malicious instructions embedded in external content can redirect agents toward unauthorized actions.
• Unauthorized API access and abuse: Excessive permissions, exposed credentials, or insecure endpoints can allow agents to access or modify restricted resources.
• Excessive agency and privilege escalation: Agents with unnecessary permissions can perform high-impact operations beyond their intended responsibilities.
• Sensitive data exposure and exfiltration: Compromised or manipulated agents can disclose confidential information to unauthorized users or external systems.
• Rogue AI agents and shadow agentic AI: Unapproved or compromised agents can operate without security oversight and access enterprise systems.
• Insecure MCP servers and tools: Untrusted Model Context Protocol integrations can expose agents to malicious instructions, credential theft, and unauthorized tool execution.
• Tool poisoning and malicious integrations: Manipulated tool descriptions, configurations, or responses can influence agent decisions and trigger harmful operations.
• Agent-to-agent trust exploitation: Compromised agents can send misleading instructions or false information to other agents in coordinated workflows.
• Business logic abuse and unauthorized transactions: Agents can misuse legitimate application functions to bypass approval rules or execute unauthorized business operations.
• Memory poisoning and persistent manipulation: Malicious information stored in agent memory can influence decisions across multiple tasks and sessions.
• Cascading failures in multi-agent systems: Errors or malicious actions can propagate between connected agents and disrupt multiple systems.
• AI-powered bot attacks and automated API abuse: Autonomous attackers can adapt their behavior to conduct credential attacks, scraping, fraud, and other automated abuse.

This is part of a series of articles about agentic AI security

In this article:

• Why Does Agentic AI Introduce New Security Risks?
• Top Agentic AI Security Risks and Threats
• Best Practices to Mitigate Agentic AI Risks

Why Does Agentic AI Introduce New Security Risks?

Autonomous Decision-Making and Execution Without Human Oversight

AI agents can independently determine which actions to take and execute them without continuous human supervision. While this improves efficiency, it also allows incorrect or malicious actions to proceed before security teams can intervene.

For example, an agent responsible for managing cloud resources might interpret a malicious instruction as a legitimate maintenance task and delete production resources. Similarly, an agent with access to financial systems could approve unauthorized transactions if its decision-making process is manipulated.

These risks are particularly serious when agents perform irreversible operations. Organizations need to implement execution limits, approval requirements for sensitive actions, and mechanisms to interrupt or reverse agent operations when possible.

Increased Access to Enterprise Applications, APIs, and Sensitive Data

AI agents often require access to multiple enterprise systems to complete tasks. These may include customer databases, cloud infrastructure, internal communication platforms, and financial applications. Agents typically interact with these resources using API keys, service accounts, or delegated user permissions.

This access creates opportunities for privilege misuse and data exposure. If an attacker compromises an agent or manipulates its instructions through prompt injection, they may be able to exploit the agent's existing permissions to retrieve sensitive records, modify configurations, or execute unauthorized operations.

The risk becomes greater when agents receive broad permissions that exceed their operational requirements. Applying least-privilege access, limiting credential scope, and validating authorization for individual actions can reduce the potential impact of agent compromise.

Complex Multi-Agent Workflows and Unpredictable Agent Behavior

Multi-agent systems use several AI agents that communicate, delegate tasks, and coordinate actions to achieve shared objectives. Each agent may have different permissions, tools, and responsibilities, creating dependencies that are difficult to monitor and secure.

Security problems can emerge when one agent passes incorrect or malicious information to another. For example, an attacker could inject instructions into a document processed by a research agent. If the research agent forwards those instructions to an execution agent, the second agent might perform an unauthorized action.

These interactions can also produce unexpected behavior when agents interpret instructions differently or repeatedly delegate tasks. Maintaining clear trust boundaries, validating messages between agents, and recording agent decisions helps organizations identify and contain these risks.

Expanded Attack Surface Through Third-Party Tools and Integrations

Agentic AI systems frequently connect to external services through APIs, plugins, browser automation, and tool integration frameworks. Each connection introduces additional components that attackers may target or manipulate.

For example, an agent using a web browsing tool may encounter a malicious webpage containing hidden instructions designed to redirect its behavior. An attacker could also compromise a third-party API or tool response to supply manipulated information that causes the agent to expose data or execute harmful commands.

These attacks are especially dangerous when agents treat external content as trusted instructions rather than untrusted data. Organizations should validate tool outputs, restrict available integrations, isolate tool execution environments, and enforce authorization checks before allowing agents to perform sensitive operations.

Top Agentic AI Security Risks and Threats

1. Prompt Injection and Agent Manipulation

Prompt injection occurs when an attacker embeds malicious instructions in content that an AI agent processes. These instructions can appear in webpages, emails, documents, database records, or tool responses. If the agent treats this untrusted content as instructions, an attacker may redirect its behavior, override task requirements, or trigger unauthorized actions.

For example, a customer support agent retrieves a support ticket containing hidden instructions to export customer records to an external URL. If the agent follows these instructions instead of treating them as ticket content, it could expose sensitive customer information.

Mitigations:

• Treat external content and tool responses as untrusted data, not authoritative instructions.
• Enforce authorization checks before agents execute sensitive operations.
• Restrict access to tools and data based on the agent's assigned task.
• Require human approval for high-risk actions, such as data exports or system changes.
• Test agents against prompt injection scenarios using adversarial security testing.

2. Unauthorized API Access and Abuse

AI agents frequently use APIs to communicate with enterprise applications, retrieve information, and perform operations. Unauthorized API access occurs when agents use exposed credentials, misconfigured permissions, or insecure endpoints to access resources beyond their intended scope.

For example, an AI agent responsible for retrieving order statuses may have access to an API that also supports refunds and order cancellations. If the agent's credentials permit these operations, an attacker could manipulate it into issuing unauthorized refunds.

Mitigations:

• Use scoped API tokens that grant access only to required endpoints and operations.
• Enforce authentication and authorization at the API level for every request.
• Apply rate limits and usage quotas to agent-generated API traffic.
• Store API credentials in secure secrets management systems and rotate them regularly.
• Monitor API activity for unusual request patterns and unauthorized operations.

3. Excessive Agency and Privilege Escalation

Excessive agency occurs when an AI agent has more permissions, tools, or operational freedom than necessary to complete its assigned tasks. This increases the potential damage from compromised instructions, incorrect decisions, or malicious inputs.

For example, an AI agent designed to analyze cloud infrastructure costs may receive administrative permissions across an entire cloud environment. An attacker who manipulates the agent could cause it to modify security configurations, delete resources, or create privileged accounts.

Mitigations:

• Apply the principle of least privilege to agent identities, tools, and API credentials.
• Separate read-only tasks from operations that modify or delete resources.
• Use temporary, task-specific credentials instead of persistent administrative access.
• Require additional authorization for privileged or irreversible operations.
• Regularly review agent permissions and remove unnecessary access.

4. Sensitive Data Exposure and Data Exfiltration

AI agents often process confidential business information, customer records, credentials, and internal documents. Sensitive data exposure occurs when agents reveal this information to unauthorized users, external services, or other agents without appropriate access rights.

For example, an AI research agent with access to internal financial reports may be manipulated through a malicious webpage into sending report contents to an attacker-controlled endpoint. The agent could perform the transfer using its legitimate network and API permissions.

Mitigations:

• Enforce data access controls based on user identity, agent identity, and task context.
• Restrict outbound network connections and data transfers to approved destinations.
• Apply data loss prevention (DLP) controls to detect sensitive information in agent outputs.
• Redact credentials, personal information, and confidential fields when they are not required.
• Maintain audit logs of sensitive data access and external transfers.

5. Rogue AI Agents and Shadow Agentic AI

Rogue AI agents are agents that operate outside their intended scope or perform unauthorized actions. Shadow agentic AI refers to agents deployed by employees or teams without formal IT approval, security assessment, or centralized oversight.

For example, an employee may deploy an autonomous agent that connects to the company's CRM, email platform, and cloud storage using personal API tokens. The agent may transfer customer information to an external AI service without the organization's knowledge or approval.

Mitigations:

• Maintain a centralized inventory of deployed AI agents, their owners, and connected systems.
• Require security reviews before agents receive access to enterprise applications.
• Monitor service accounts, API tokens, and integration activity to identify unauthorized agents.
• Establish policies for approved agent development platforms and external AI services.
• Implement centralized controls to disable or revoke access for compromised or unauthorized agents.

Related content: Read our article about agentic AI governance

6. Insecure Model Context Protocol (MCP) Servers and Tools

The Model Context Protocol (MCP) provides a standardized way for AI applications to connect with external tools and data sources. Insecure MCP implementations can expose agents to unauthorized tool execution, credential theft, data leakage, and malicious server responses.

For example, an AI coding agent may connect to an untrusted MCP server that advertises a file management tool. If the server provides misleading tool descriptions or requests excessive permissions, the agent could be manipulated into accessing sensitive files or sending their contents to the server.

Mitigations:

• Connect agents only to approved and verified MCP servers.
• Authenticate MCP clients and servers, and enforce authorization for individual tool operations.
• Restrict MCP server permissions and isolate execution environments.
• Review tool definitions, descriptions, and updates for malicious instructions.
• Monitor MCP tool calls and block unexpected access to sensitive resources.

7. Tool Poisoning and Malicious Third-Party Integrations

Tool poisoning occurs when an attacker modifies tool definitions, descriptions, outputs, or other integration content to influence an AI agent's behavior. Since agents use tool metadata to decide which tools to invoke and how to use them, malicious content can redirect execution toward attacker-controlled operations.

For example, an attacker compromises a third-party search integration and inserts instructions into its tool description. The instructions tell the agent to retrieve internal configuration files before completing a search request. An agent that follows the poisoned description may expose sensitive information.

Mitigations:

• Verify the origin and integrity of third-party tools and integration packages.
• Review tool descriptions, schemas, and permission requests before deployment.
• Pin approved tool versions and monitor changes to tool configurations.
• Isolate third-party tools from sensitive systems and credentials.
• Validate tool outputs and enforce independent authorization before executing requested actions.

8. Agent-to-Agent Communication and Trust Exploitation

Multi-agent systems rely on communication between agents to exchange information, delegate tasks, and coordinate workflows. Trust exploitation occurs when a malicious or compromised agent sends instructions or misleading information that another agent accepts without sufficient verification.

For example, a compromised research agent may send a message to a financial operations agent claiming that a payment has been approved. If the receiving agent trusts the message without verifying the approval, it could initiate an unauthorized transaction.

Mitigations:

• Authenticate agent identities and protect communication channels.
• Apply authorization controls to messages that request sensitive operations.
• Validate instructions and data received from other agents before acting on them.
• Separate agent responsibilities to prevent one compromised agent from controlling an entire workflow.
• Log inter-agent messages, delegated tasks, and resulting actions for security investigations.

9. Business Logic Abuse and Unauthorized Transactions

Business logic abuse occurs when an AI agent uses legitimate application functions in ways that violate business rules or intended workflows. Unlike attacks that exploit software vulnerabilities, these attacks manipulate how agents interpret goals, apply policies, or sequence authorized operations.

For example, an AI purchasing agent may be instructed to optimize procurement costs. An attacker could manipulate supplier information or approval messages to make the agent place orders with an unauthorized vendor or exceed approved spending limits.

Mitigations:

• Enforce transaction limits, spending thresholds, and approval rules outside the AI model.
• Validate business conditions before executing payments, refunds, or account changes.
• Require human approval for high-value or unusual transactions.
• Use separation of duties for initiating, approving, and executing sensitive operations.
• Monitor transaction patterns for unusual amounts, recipients, or frequencies.

10. Memory Poisoning and Persistent Agent Manipulation

Memory poisoning occurs when an attacker inserts false information or malicious instructions into an AI agent's persistent memory or retrieved knowledge. Unlike a prompt injection that affects a single interaction, poisoned memory can influence the agent's behavior across multiple tasks or sessions.

For example, an attacker may manipulate a support agent into saving a false policy stating that certain customers are eligible for automatic refunds without verification. The agent could subsequently apply this incorrect policy to future requests, causing repeated unauthorized refunds.

Mitigations:

• Restrict which users, tools, and workflows can modify persistent agent memory.
• Validate information before storing it in long-term memory or knowledge repositories.
• Track the source, timestamp, and modification history of stored information.
• Separate trusted policies and system instructions from agent-generated memories.
• Periodically review stored memories and remove suspicious or outdated entries.

11. Cascading Failures in Multi-Agent Systems

Cascading failures occur when an error, compromised agent, or malicious instruction propagates through a multi-agent workflow. Because agents often depend on the outputs and decisions of other agents, one failure can trigger additional failures across connected systems.

For example, a monitoring agent incorrectly reports that a production server is compromised. A remediation agent responds by shutting down the server, while another agent automatically redirects traffic to an unprepared backup environment. The combined actions could cause a service outage.

Mitigations:

• Establish clear execution boundaries and limits for individual agents.
• Implement circuit breakers to stop workflows when abnormal behavior is detected.
• Require independent verification before high-impact actions are propagated to other agents.
• Use staged execution and testing environments for potentially disruptive operations.
• Implement rollback procedures and recovery mechanisms for automated changes.

Related content: Read our article about agent containment

12. AI-Powered Bot Attacks and Automated API Abuse

Attackers can use autonomous AI agents to conduct bot attacks and automate API abuse. Unlike traditional bots that follow predefined scripts, AI-powered bots can adapt their requests based on application responses, discover accessible functionality, and coordinate multiple attack steps.

For example, an attacker may deploy AI agents to create fraudulent accounts, test stolen credentials, scrape protected information, or abuse promotional offers. These agents can adjust their behavior when requests fail, making simple rule-based detection less effective.

Mitigations:

• Apply rate limiting and request quotas based on user identity, API credentials, and behavioral signals.
• Use bot detection and behavioral analysis to identify automated abuse.
• Implement risk-based authentication and additional verification for suspicious activity.
• Detect abnormal account creation, login attempts, scraping, and transaction patterns.
• Monitor API traffic and correlate activity across accounts, sessions, and network sources to identify coordinated attacks.

Best Practices to Mitigate Agentic AI Risks

Here are some of the ways that organizations can overcome the risks associated with AI agents.

1. Establish Complete Visibility into AI Agents and Shadow AI

Organizations need a complete inventory of AI agents operating across their environments, including agents deployed without formal IT or security approval. Without centralized visibility, security teams may not know which agents access sensitive data, what permissions they hold, or which external services they communicate with.

Agent discovery should cover:

• Cloud environments
• Enterprise applications
• API integrations
• Development platforms

Each agent should have an identifiable owner, documented purpose, assigned permissions, and a list of connected resources. Security teams should also monitor API traffic, service account activity, and application integrations to detect shadow AI deployments. Maintaining an up-to-date agent inventory makes it easier to identify unauthorized agents, review their permissions, and revoke access when necessary.

2. Implement Zero Trust Security for Agentic AI Systems

Zero Trust security assumes that no agent, user, device, or application should be trusted automatically, regardless of its network location. Every request must be authenticated, authorized, and evaluated against applicable security policies. For agentic AI, this means verifying an agent's identity and permissions before allowing it to:

• Access data
• Invoke tools
• Perform operations

Trust should not automatically transfer between agents, even when they belong to the same multi-agent workflow. Organizations should enforce authorization at every resource rather than relying solely on instructions provided to the AI model. This prevents a compromised agent from using its position within a trusted workflow to gain unrestricted access to connected systems.

Zero Trust in its original form assumes human users, managed devices, and predictable workloads, while agents reason, delegate to other agents, and act at machine speed. Agentic Zero Trust, a framework published by Dr. Chase Cunningham of DrZeroTrust, extends the model with a behavioral check: beyond confirming who an agent is and whether an action is permitted, it verifies that the agent is still doing the job it was deployed for. An action that passes authorization but doesn't fit the agent's purpose still fails verification.

3. Enforce Least-Privilege Access to APIs, Tools, and Enterprise Resources

AI agents should receive only the permissions required to complete their assigned tasks. Excessive permissions increase the damage an agent can cause if its instructions are manipulated, its credentials are compromised, or it makes an incorrect decision. For example, an agent that analyzes cloud infrastructure costs should have read-only access to billing and resource information. It should not receive permissions to:

• Delete virtual machines
• Change security policies
• Create administrative accounts

Organizations can enforce least privilege through role-based access control (RBAC), attribute-based access control (ABAC), scoped API tokens, and temporary credentials. Permissions should also be reviewed regularly and revoked when agents no longer need them.

4. Secure Agent-to-API Communication with Strong Authentication and Authorization

AI agents rely on APIs to retrieve information, interact with applications, and execute business operations. Securing these connections is essential because compromised API credentials or weak authorization controls can allow agents to access sensitive resources or perform unauthorized actions.

Each agent should use a distinct identity with scoped credentials rather than shared API keys. To authenticate agents and protect communication channels, organizations can use:

• OAuth 2.0
• Short-lived access tokens
• Mutual TLS (mTLS)

API authorization must also be enforced independently of the agent's decisions. Before processing a request, the API should verify that the agent is permitted to access the requested resource and perform the specified operation.

5. Deploy an AI Gateway for Centralized Security and Policy Enforcement

An AI gateway provides a centralized control point for managing traffic between AI agents and the models, APIs, MCP servers, and enterprise applications they use. It can help organizations enforce consistent security policies, monitor AI interactions, and control which models, tools, and APIs agents can reach. For example, an AI gateway can:

• Authenticate incoming requests
• Apply usage limits
• Inspect prompts and responses for sensitive information
• Block connections to unauthorized AI services

It can also maintain logs that help security teams investigate suspicious interactions. An AI gateway protects only the traffic routed through it, so organizations should route agent-to-tool and agent-to-API traffic through the gateway along with model traffic, and keep authorization checks in place at the destination systems.

6. Secure MCP Servers and Restrict Access to Trusted Tools

Model Context Protocol (MCP) servers allow AI agents to interact with external tools, applications, and data sources. These connections can introduce security risks when servers are untrusted, tool definitions contain malicious instructions, or permissions are configured too broadly.

Organizations should maintain an approved registry of MCP servers and tools. Before deployment, security teams should:

• Verify server origins
• Review tool capabilities
• Inspect requested permissions
• Assess how the server handles credentials and sensitive data

MCP servers should run with restricted permissions and isolated execution environments where possible. Organizations should also authenticate connections, enforce authorization for tool calls, monitor tool usage, and review changes to tool definitions to detect potential tool poisoning.

7. Continuously Monitor Agent Behavior and Detect Anomalous Activities

Because AI agents can execute multiple actions independently, security teams need continuous visibility into their behavior. Monitoring should cover:

• Tool invocations
• API requests
• Data access
• Permission changes
• Inter-agent communication
• External network connections

Behavioral monitoring can help identify suspicious activity, such as an agent suddenly accessing large volumes of customer records, invoking unfamiliar tools, or repeatedly attempting operations outside its assigned responsibilities.

Organizations should establish behavioral baselines for individual agents and apply anomaly detection to identify deviations. High-risk events should trigger alerts or automated responses, such as suspending tool access, revoking credentials, or requiring human approval before execution continues.

8. Protect APIs Against Automated Abuse and Business Logic Attacks

Agentic AI can generate large volumes of API requests and execute complex workflows without human intervention. Attackers can exploit these capabilities to automate:

• Account creation
• Credential attacks
• Data scraping
• Fraudulent transactions
• Other forms of API abuse

Traditional rate limiting helps control request volumes, but it may not detect attacks that use valid credentials or remain within normal traffic thresholds. Organizations also need behavioral analysis to identify suspicious sequences of API calls and unusual business operations.

Security controls should include per-identity rate limits, transaction thresholds, bot detection, and business logic validation. Sensitive operations, such as payments, refunds, and account modifications, should require independent authorization and additional verification when risk indicators are detected.

9. Implement Data Loss Prevention (DLP) Controls for AI Agent Interactions

AI agents may process sensitive information from databases, documents, internal applications, and user conversations. Without appropriate controls, this information can be exposed through:

• Model prompts
• Generated responses
• Tool outputs
• External API requests

Data loss prevention (DLP) controls help identify and restrict the movement of confidential information. Organizations can inspect agent inputs and outputs for personal information, credentials, financial records, and other sensitive data before allowing it to reach external systems.

DLP policies should be applied at relevant data access and transfer points, including AI gateways, API gateways, and tool integrations. Organizations should also use data classification, redaction, and outbound destination restrictions to prevent unauthorized data disclosure.

10. Apply Runtime Guardrails and Context-Aware Security Policies

Runtime guardrails are security controls that evaluate agent actions while workflows are executing. Unlike static instructions that tell an agent what it should or should not do, runtime controls can block operations that violate security policies before they reach sensitive systems.

For example, an agent may be permitted to retrieve customer account information but prohibited from exporting an entire customer database. A runtime policy can evaluate the requested operation, agent identity, data sensitivity, and destination before deciding whether to allow it. Context-aware policies should consider the agent's:

• Assigned task
• Current permissions
• Requested tool
• Target resource
• Potential impact of the action

Organizations can combine these checks with execution limits, human approval requirements, and automated intervention to prevent unsafe actions while allowing legitimate workflows to continue.

Mitigating Agentic AI Risks with Cequence AI Gateway

Cequence AI Gateway provides the security, governance, and control enterprises need to deploy agentic AI workflows at scale. It treats every agent with access to applications and data as a privileged insider operating at machine speed, constraining each agent to its job description and evaluating every action in context to prevent agent overreach. Delivered as a SaaS-based solution with on-premises deployment also available, it connects agents to enterprise applications through native Model Context Protocol (MCP) support while enforcing policy inline on every tool call.

Key capabilities of Cequence AI Gateway:

• Agentic Zero Trust architecture: Authenticates agents and then verifies every action they take, enforcing policy inline in the request path for the full session and on every tool call.
• Agent Personas: Lets teams define an agent's job description in plain English to generate a persona with only the tools, APIs, skills, and permissions it needs, enforcing least privilege automatically.
• Prompt injection protection: Prompt Guard screens LLM prompts for injection, jailbreak, and system-prompt-extraction patterns before they reach the model, with support for custom rules.
• Trusted registries and runtime guardrails: Provides trusted registries of MCP servers, APIs, LLMs, and skills, plus automated tool risk scoring, rate limiting, and spend limits that control what agents can reach and consume.
• End-to-end authentication and authorization: Integrates with OAuth 2.1-compliant identity infrastructure and includes built-in token lifecycle management to prevent unauthorized agent access to systems and data.
• Sensitive data protection: Applies DLP scanning to agent requests and MCP server responses, with the ability to monitor, redact, or block sensitive data across more than 100 out-of-the-box detection types.
• AI discovery: Surfaces sanctioned and shadow AI enterprise-wide, including every shadow MCP server, agent, and LLM provider found in existing SIEM logs.
• Monitoring and audit trails: Delivers real-time visibility into AI-tool traffic with full audit logging of user, agent, and tool behavior, the applications being accessed, and the API calls agents make.
• Protection from agent-fueled attacks: Integration with Cequence API Security and Bot Management protects applications and APIs from agent-fueled attacks, abuse, and fraud.

Learn more about Cequence AI Gateway and see how it helps you secure agentic AI without slowing adoption.