AI agents bring efficiency and new cyber risks to healthcare

Hospitals are adopting AI agents to ease administrative strain, but their integration is opening new doors for cyberattacks.

What happened

Healthcare facilities across the United States are deploying AI agents to manage tasks like scheduling, billing, diagnostics support, and patient communication. While these tools help streamline operations in understaffed environments, they also introduce new vulnerabilities, particularly when connected to sensitive systems via frameworks like Model Context Protocol (MCP).

Security researchers and industry experts now warn that AI agents, when improperly secured, can be exploited by attackers to gain access to medical records, staff calendars, internal emails, and even financial systems. In some cases, attackers can manipulate agents through engineered prompts without ever breaching traditional firewalls.

 

Going deeper

AI agents are distinct from general-purpose AI tools. They’re configured with a specific objective, equipped with a ‘brain’ powered by a language model, and have access to digital tools or platforms needed to perform that objective. In a healthcare context, this can include scheduling patient visits, flagging clinical errors, or interfacing with insurance providers.

Agents powered by MCP frameworks can span systems from patient databases to building infrastructure, providing operational convenience but creating cyber risk through interconnected exposure. A compromised agent can autonomously interact with multiple systems at once, often without immediate detection.

The broader risk lies in how these agents communicate. If adversaries exploit MCP’s integration channels, a malicious command can be passed along to multiple agents or systems rapidly, much like a bloodstream spreading toxins. Unlike traditional malware, which might need to escalate access levels, a hacked AI agent already holds operational permissions.

 

What was said

Cybersecurity professionals recommend pre-integration audits, where agents are tested for vulnerabilities using red teaming techniques and prompt-based simulations. Multi-layered defenses, including encryption, access controls, and network segmentation, are also advised, along with ongoing monitoring.

Security teams are encouraged to treat agentic systems as dynamic risk factors that require continual evaluation. In particular, the principle of “least privilege” should guide how agents are granted access to internal systems.

 

FAQs

What makes AI agents different from standard AI tools in healthcare?

Unlike passive AI tools, agents can independently take action by accessing software tools and datasets to fulfill assigned objectives like scheduling, referrals, or billing support without human input.

 

Why is Model Context Protocol (MCP) a cybersecurity concern?

MCP enables streamlined cross-platform access, but this interconnectivity can be exploited by attackers to move malicious commands or data across systems quickly and undetected.

 

How can attackers manipulate AI agents without hacking the full network?

Through prompt engineering, carefully crafted inputs that trick agents into performing harmful actions, they can bypass conventional security layers and execute tasks using the agent’s own permissions.

 

What is red teaming in the context of AI security?

Red teaming involves simulating attacks on AI systems using adversarial prompts, misuses, and exploits to identify vulnerabilities before real-world attackers can take advantage.

Source: https://www.paubox.com/blog/ai-agents-bring-efficiency-and-new-cyber-risks-to-healthcare