What is the difference between an AI agent and a chatbot?

A chatbot primarily generates text responses to user prompts, while an AI agent can plan and execute multi-step tasks using external tools with limited human oversight. Agents actively interact with applications, accounts, and devices to complete objectives rather than only conversing. This autonomy enables action but increases the scope of potential security incidents.

What is indirect prompt injection in AI agents?

Indirect prompt injection hides malicious instructions inside data an agent processes, such as emails, websites, or documents, rather than in direct user input. The agent may treat those instructions as legitimate context and perform unauthorized actions like forwarding sensitive files. This risk grows when agents have access to tools and permissions.

What is memory poisoning in AI agents?

Memory poisoning occurs when an attacker inserts false or malicious information into an agent's persistent memory store. The agent later treats that stored content as trusted background context and may act on it in future sessions. Because the corrupted memory persists, the impact can survive across interactions and evade immediate detection.

How can organizations monitor AI agents for unusual behavior?

Companies should log important agent actions such as tool calls, data access, and external communications to establish a baseline. Sudden spikes in activity, unexpected outbound messages, or repeated failures can indicate abuse or malfunction. Continuous monitoring combined with human review of high-risk steps helps detect drift from intended purpose.

AI Agent Risks: What Users and Businesses Should Know

Q: What is shadow AI and why is it risky?

Shadow AI refers to unauthorized or undocumented AI agents that employees deploy outside official IT and security controls. These agents may have excessive permissions, insecure configurations, or access to sensitive data that security teams cannot see. The lack of visibility creates blind spots that hinder incident response and increases exposure to compromise.

Tech companies are now promoting AI agents as the next big thing after chatbots. The technology is already moving rapidly into mainstream and business environments. According to McKinsey's 2026 State of AI survey, 40% of enterprise applications are expected to include task-specific AI agents by the end of 2026, up from 27% in 2025. As AI agents become more capable and gain access to more of our data, accounts, devices, and online services, the consequences of mistakes or abuse grow much larger. As businesses and individuals give these systems more autonomy, they also need to understand what can go wrong, how attackers could abuse AI agents, and what safeguards can reduce the risk.

What is an AI agent?

An AI agent is a software system that can understand a goal, make decisions, use tools or external systems, and take multiple steps to complete a task with limited human intervention. Unlike a traditional chatbot, which responds to user prompts with generated text, an AI agent can actively perform actions on the user’s behalf, such as searching websites, managing schedules, analyzing data, sending messages, or interacting with other applications. In simple terms, a chatbot mainly talks, while an AI agent can plan, act, and complete tasks. The appeal of AI agents is easy to understand. People and businesses already have plenty of software. The big challenge is that people often spend a lot of time moving information between applications and checking different apps before making decisions. AI agents promise to reduce this friction. An agent can gather the necessary information, use connected tools, and move a task forward with less human involvement. This can help people and organizations save time, improve efficiency, and reduce the amount of manual work they have to handle.

Security risks of AI Agents

1. Prompt injection attacks

Imagine a company gives an AI agent access to an employee's inbox so it can summarize incoming emails. The employee receives a malicious email containing hidden instructions telling the AI to search the mailbox for sensitive documents and forward them to an external address. The employee may never see anything suspicious. The AI agent, however, could interpret the malicious instructions as part of the information it is processing. This is known as indirect prompt injection. The malicious instructions do not necessarily come directly from the user. They can be hidden inside emails, websites, documents, search results, or other external data. Prompt injection is already a major risk for generative AI, but the consequences can become more serious when an AI system has tools and permissions.

2. Memory poisoning

Memory is one of the features that can make AI agents more useful. An agent that remembers previous interactions does not have to start from scratch every time. But persistent memory also creates another place where attackers can try to manipulate the system. An attacker may insert malicious or misleading information into an agent's memory. Later, the agent might treat that information as trusted context. This is known as memory poisoning. Imagine an employee uses an AI agent to manage projects. Someone manages to insert false instructions into its persistent memory, such as a fake rule saying that a particular external account is authorized to receive company documents. If the agent trusts that memory in a future session, the attacker could benefit long after the original interaction has ended.

3. Rogue agents

Sometimes AI agents may drift away from their intended goals or instructions and begin taking actions that were not expected by their users or developers. In a recent case, independent researchers discovered several websites where AI agents believed to have been created by OpenAI appeared to take actions without authorization. The agents reportedly accessed websites, posted messages, and exchanged data with one another. The findings have raised further concerns about whether AI developers can effectively control increasingly autonomous agentic systems and prevent them from acting beyond their intended boundaries.

4. Agent goal hijack

Agent goal hijacking occurs when an attacker manipulates an agent into changing or abandoning its intended objective. Unlike prompt injection, which targets a specific response from an AI model, goal hijacking can influence an agent’s broader, multi-step behavior. Attackers may use manipulated prompts, malicious data, compromised information sources, or fake communications to redirect an agent and make it perform actions that were never intended by its user. The risk exists because AI agents often rely on natural-language instructions and automated decision-making to determine their next steps. An attacker can therefore create content to influence the agent’s planning or decision-making without directly interacting with the user. If successful, the agent could be redirected toward actions that expose sensitive information, misuse connected tools, or otherwise benefit the attacker at the expense of the user or organization.

5. Supply Chain Vulnerabilities

AI agents often depend on a complex supply chain that includes third-party models, plugins, APIs, libraries, tools, datasets, and external services. This creates more attack surface for attackers to compromise an agent. A malicious or compromised dependency could manipulate an agent’s behavior, steal sensitive information, introduce malicious instructions, or gain access to connected systems. The risk becomes greater when organizations use agents built from components they do not fully control or understand. If one trusted component in the AI supply chain is compromised, the attacker could use that trust to affect many downstream agents and organizations.

6. Insecure inter-agent communication

Insecure inter-agent communication occurs when multiple AI agents exchange information or instructions without strong authentication, authorization, and validation controls. In a multi-agent system, one agent relies on information received from another. This creates an opportunity for an attacker to modify messages, inject malicious instructions, or manipulate the information being shared. For example, if a research agent sends findings to a purchasing agent through an unencrypted channel, an attacker who compromises that communication channel could alter the data and cause the purchasing agent to make an unauthorized decision. Because agents can automatically trust and act on information received from other agents, a compromised agent or communication channel could spread malicious instructions across an entire workflow.

7. AI agents can leak sensitive information

AI agents often need access to information to perform their tasks. That information could include customer records, employee data, financial documents, passwords, internal communications, intellectual property, or confidential business plans. The problem becomes more complicated when an agent can combine information from multiple sources. For example, an employee might ask an AI assistant to prepare a customer report. The agent then accesses a CRM, internal documents, email messages, spreadsheets, and other systems. If those systems contain information the employee was never supposed to combine, the AI could unintentionally bring that information together. There is also a risk that sensitive information could appear in agent logs, tool calls, outputs, or persistent memory.

8. Shadow AI

Sometimes employees use unauthorized or undocumented AI agents that operate outside an organization’s official IT and security controls. Individuals may use these agents to automate tasks or improve productivity. This can create major risks, as these unapproved agents may have excessive permissions, insecure configurations, or access to sensitive data that the organization is not aware of. Shadow AI also makes it harder for security teams to maintain visibility across the organization. This can create security blind spots and make it more difficult to identify, investigate, and respond to incidents when something goes wrong.

What users should do

Consumers do not necessarily need to avoid AI agents. They should, however, treat them more like powerful digital assistants than ordinary chatbots.

Give agents limited access

Do not connect an AI agent to every account simply because integration is available. Only provide access to the services it actually needs.

Review important actions

Keep humans involved when an agent wants to make purchases, send sensitive information, change account settings, delete files, or perform other irreversible actions.

Keep connected accounts secure

Use strong authentication and monitor connected accounts for unusual activity. If an AI agent is compromised, the accounts connected to it could become the next target.

What businesses should do

The human should remain in control

Organizations should require human approval for high-risk activities such as accessing sensitive information, making financial decisions, changing critical systems, sending external communications, or executing code. Human oversight can also help detect unusual behavior and identify situations where an agent is operating outside its intended purpose. Humans should remain in control of decisions that could have serious security, financial, or operational consequences.

Apply least privilege

Every agent should receive the minimum permissions necessary to perform its job. A reporting agent should not have administrator access. A document-reading agent should not have deletion privileges.

Monitor agent activity

Companies should log important agent actions and monitor for unusual behavior. Sudden increases in tool calls, unusual data access, unexpected external communications, or repeated failed actions could indicate abuse or malfunction.

Have an emergency shutdown process

Companies should know how to quickly disable an agent and revoke its credentials. This becomes important when agents can operate without continuous human supervision.

AI Agent Risks: What Users and Businesses Should Know

What is an AI agent?