The Common Security Risks Associated with Agentic AI

重要提示

• Agentic AI expands the attack surface through greater autonomy and access to external systems.
• AI agent security is no longer just about who accesses data, but what AI agents are allowed to do with it.
• Top AI agent security risks include indirect prompt injections, privilege escalation, supply chain attacks, agentic looping, and hallucinated references.
• Countering these risks effectively requires proactive measures and a shift from traditional perimeter scanning to “runtime reasoning governance.”
• Vital security controls to implement include reasoning sandboxes, behavioral monitoring, kill switches to sever the connection to external tools, and red teaming to test for vulnerabilities

The Business Value of Agentic AI

AI agents are smart software systems with the ability to monitor and respond to their environment. Rather than the prompt and response chatbot-style interaction of large language models, AI agents can connect to external tools and systems in order to take actions autonomously based on a specific objective. Autonomous or semi-autonomous AI agents offer significant business benefits in a range of use cases, including:

• • Customer Support: Managing inquiries and providing better support around the clock.
  • Administrative Tasks: Automating repetitive internal processes and freeing up staff to focus on more creative work.

• IT Operations: Monitoring systems to detect anomalies and take corrective actions that minimize downtime.

• Supply Chain Management: Predicting demand, managing inventory, and coordinating logistics in real-time to optimize operations.

• Marketing: Analyzing customer interactions to tailor personalized marketing messaging and improve engagement.

These are just a few examples of how AI agents are transforming business workflows. A PWC survey of senior executives found that 79% are already adopting AI agents in their operations and 66% have measured increased productivity from their use. As use of the technology grows, organizations must understand the new AI agent security risks it poses and how it expands the attack surface.

How Agentic AI Expands Your Attack Surface

Greater levels of autonomy combined with access to external systems completely change the security implications of AI systems. Agents perform actions without human oversight, often in real time, amplifying vulnerabilities and increasing the damage they can cause. An AI agent doesn’t simply provide information or generate content when prompted. It interprets an objective and determines the best actions to achieve it.

The agentic workflows behind these actions are inherently non-deterministic. This creates a “Black Box” problem, where the same input can trigger different responses and calls to external tools depending on subtle context changes. Without the clearly defined workflows and causal links between inputs and outputs that are present in traditional software, it is difficult to predict and audit behavior or implement effective security controls.

Another AI agent security risk to consider is the access control gap. To enable autonomous action, many enterprises grant agents broad “Service User” permissions, including read/write access. This violates the principle of least privilege, giving agents the ability to both access and change sensitive information in external data stores. This also creates opportunities for autonomous privilege escalation, where agents combine seemingly permissible tools to achieve impermissible outcomes. For example, reading calendars and sending emails to exfiltrate sensitive meeting notes.

5 of the Most Pressing AI Agent Security Risks

As agentic AI becomes increasingly integrated into enterprise workflows, organizations must develop comprehensive AI Security Posture Management (AI-SPM) frameworks to address and mitigate the new security risks introduced. By understanding how improper agent use creates unnecessary risk and how attackers hack AI agents, businesses can implement security controls, practices, and technologies to minimize their exposure.

Listed below are five of the most pressing AI agent security risks that organizations must consider in order to adopt the technology safely.

Risk 1: Indirect Prompt Injection

An indirect prompt injection is when attackers embed malicious instructions into seemingly legitimate documents. When Retrieval-Augmented Generation (RAG) pipelines access these documents, the agent unknowingly executes the hidden command, interpreting the instructions found as part of its normal processing.

Once executed, the hidden commands can carry out unauthorized actions, leading to data breaches or business disruption. Examples include forwarding sensitive emails, exfiltrating documents, or triggering API calls to external endpoints. Indirect prompt injections can impact AI agents because they can act without human oversight, effectively turning routine document ingestion into an automated attack vector that compromises enterprise systems.

Risk 2: Privilege Escalation

Privilege escalation exploits the gap between user permissions and agent capabilities. In the attack, agents with high-level access are tricked into executing commands on behalf of unauthorized or low-privileged users. They increase their level of access or escalate their privileges.

An example of privilege escalation could be a junior employee asking an HR AI agent for the company’s complete salary table. The agent, authorized to access this data, executes the request, effectively elevating the employee’s access rights without direct hacking. The agent bypasses the employee’s limited permissions, effectively escalating privileges without direct exploitation of the system.

Risk 3: Model Context Protocol (MCP) Supply Chain Attacks

Agents connect to external tools and APIs, such as Slack, GitHub, Jira, etc., via the Model Context Protocol (MCP). These third-party tools offer another entry point for attackers to inject malicious instructions or request excessive permissions that the agent may automatically grant. This allows attackers to indirectly manipulate agent behavior, accessing sensitive data or performing harmful actions within enterprise systems.

Supply chain risks multiply as agents rely on an increasing number of third-party integrations. For example, a weather plugin integrated via MCP unexpectedly requests “File Read” permissions. The agent blindly grants access, unintentionally exposing internal financial reports stored on the same system.

Additionally, many MCP servers themselves are improperly configured. Check Point’s 2026 Cyber Security Report found that 40% of MCP servers analyzed were found to be vulnerable. This included exposing secrets and enabling code execution.

Risk 4: Agentic Looping (Denial of Wallet/Service)

In this AI agent security risk, the agent is tricked into entering a recursive reasoning loop, repeatedly calling paid APIs or provisioning cloud resources in an attempt to solve an impossible task. These loops can incur high costs, waste cloud infrastructure resources, and cause operational deadlocks, effectively creating a Denial-of-Service condition within internal systems. Unlike traditional DDoS attacks, agentic looping is caused by the agent’s internal, autonomous decision-making workflows rather than by malicious network traffic.

Risk 5: Hallucinated Object References

Agents may invent file paths, database entries, or object IDs they expect to exist based on patterns in their training data. Attempting to access or create resources that don’t exist can corrupt databases, generate misleading logs, or inadvertently expose sensitive error messages. Hallucinated actions can also propagate to cause downstream effects, creating security issues and compromising data integrity.

How to Safely Utilize Agentic AI

These threats illustrate how agentic AI expands the attack surface beyond traditional systems. Mitigating AI agent security risks requires a fundamental shift in defense strategy, from traditional perimeter-based security to a focus on runtime reasoning governance and overseeing not just access but the decisions an agent can make without a human in the loop.

Key agent AI security controls and practices to implement for the safe use of the technology include:

• Identity-Centric Control: Every agent session should have a unique, ephemeral identity, inheriting only the exact permissions of the prompting user rather than a broad service account. This Zero Trust approach ensures that agents cannot exceed their intended authority, closing the access control gap that often enables privilege escalation.
• Reasoning Sandboxes: Implementing an intermediate layer that simulates the outcome of a “Tool Call” (e.g., dry-running a DELETE SQL command) and blocks it if it violates safety policies before execution. Tools like Check Point AI Agent Security provide an effective intermediate simulation layer to minimize the risks associated with risky AI agent tool calls.
• Behavioral Drift: Continuous monitoring for behavioral drift is essential. Alerts should trigger when an agent begins accessing data types, like PII or financial records, that it has historically ignored. This real-time insight helps detect anomalies that may indicate exploitation attempts, indirect prompt injections, or other AI-driven threats.
• MCP Security: Various MCP security best practices can help minimize the risks associated with servers and the third-party tools they provide access to. Practices include encryption for all data flowing through MCP servers, behavioral analysis to detect suspicious activity, comprehensive event logs, Zero Trust verification and authentication controls, and isolating systems as much as possible to prevent breaches from spreading.
• Red Teaming Mandate: Organizations should transition from standard penetration testing to agentic swarming, where attacker agents continuously probe internal agents for logic gaps, indirect prompt injections, or privilege escalations. This approach exposes vulnerabilities that static tests often miss, keeping defenses aligned with the dynamic behavior of autonomous systems.
• The “Kill Switch”: Establishing an automated mechanism to sever agent access to MCP tools instantly upon detection of anomaly scores above a certain threshold. By implementing a kill switch to prevent a suspicious AI agent from accessing third-party tools, organizations can prevent the escalation of risks such as agentic looping, hallucinated actions, or supply chain compromises.

Confidently Roll Out AI Agents with Check Point AI Security

To ensure organizations can get the most out of AI agents without exposing critical business operations and sensitive data, Check Point has developed extensive AI security solutions. This includes AI Agent Security to:

• Discover GenAI use cases and provide real-time visibility of potential AI Agent risk across all of your organization.
• Identify and stop malicious behavior and actors in real time.
• Implement guardrails that block data leakage.
• Comply with various policies and frameworks to improve security and your reputation.

Learn more by scheduling a demo of Check Point AI Security or downloading our latest AI security report to the basics of securing AI systems.