When Agents Chain Tools, The Risk Multiplies

Over-privileged access is one of the oldest risks in enterprise security. We’ve seen it with IT accounts, in the cloud, and when integrating SaaS. Now the issue is reemerging among AI agents, where it could take on a malicious twist.

Traditional software calls one tool at a time, but AI agents are designed to chain together multiple tools, APIs, and plugins in sequence to complete a task. Each tool may be secure in isolation, but when you chain them together, they can create new vulnerabilities, potentially exposing sensitive data, bypassing compliance rules, or triggering unintended actions.

Hidden Risks in Chained Tool Flows

MCP Tools are shifting the security perimeter. The risk lies not in a tool, but in the sequence or chain of calls an agent can make at runtime. One tool’s output becomes the next tool’s input, and what starts as a benign workflow becomes a path to exploitation. Some examples of this could include:

• An agent pulling customer records from Salesforce, then feeding them into an external analytics API
• A patient support bot with access to EHRs and a third-party sentiment tool. By routing health records into the external service, the agent could unintentionally create a HIPAA violation and a costly compliance failure from an otherwise routine workflow.
• A marketing assistant that moves from using an unsafe plugin to accessing a privileged database

The agent isn’t malicious, but a chain of actions without safeguards can have harmful consequences.

Unintended Enterprise Risks of Over-Privileged AI Agents

For enterprises, the real concern isn’t that AI agents are malicious, it’s that they can accidentally introduce new vulnerabilities with damaging consequences. Simon Willison calls this the “lethaltrifecta”: three conditions, private access data, exposure to untrusted content and external communication, when combined, can turn a helpful agent into a liability.

1. Lateral Movement: A compromised or unreliable plugin becomes a stepping stone into high-value systems.
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2. Data Exfiltration and Leaks: When you access sensitive data with one tool, it can slip out through another too.
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3. Compliance and Policy Violations: Agents can chain tools in away that look fine in isolation but taken as whole, can break governance and regulatory rules.

AI agents don’t have to be malicious to cause trouble. Left unsupervised, they can leak sensitive data, give access to the wrong systems, or trigger compliance failures that could disrupt operations and damage reputation

A New Model of Control: Understanding & Securing Tool Flows in AI

The answer isn’t to ban AI agents or confine them to rigid workflows, that only limits their value. Instead, we need dynamic controls in real time that keeppace with how agents actually work:

1. Least Privilege by Default: Don’t give blanket access to agents. Grant access to the tools they need, for as long as they need them.
2. Real-time Validation: Continuously check tool calls in real time. What tool is being used? What’s the context? What data is it referencing? Are sequences of tool calls contributing to harmful flows that escalate undesired privileges?
3. Continuous Policy Enforcement and Observability: Rather than trusting the chain to do the right thing, you need to keep an eye on how tools are being used together and block unsafe mixes before they lead to problems.

Just as cloud security evolved away from unneeded, always-on access and toward enforcement at runtime, we need to take similar steps to secure how AI agents act. Understanding flows of tools is not as simple as defining sequences that may or may not be problematic but the core problem is in deeply understanding the semantic meaning of the tools being invoked, the context under which they are invoked and understanding the flows that may result in toxic or unprivileged outcomes.

Javelin offers a comprehensive platform for AI security, delivering end-to-end protection across the entire agentic flow through deep semantic analysis. It combines offense and defense: blocking unsafe actions in real time while providing the visibility and auditability to prove compliance and investigate incidents. By validating every decision as it happens, Javelin closes the gaps that static controls miss and gives organizations the confidence to scale AI securely.

Looking Ahead

Privilege escalation in AI is no longer a theory - it showing up in real world AI deployments. As the number of tools in your ecosystem grows, so does the number and complexity of possible chains, and the harder it becomes to anticipate what issues can arise.

The next frontier of AI security is flow-aware detection, understanding how tools combine. We’ll be sharing more soon on how to catch dangerous flows before they execute by analyzing sequences rather than tools in isolation. Talk to us for more details!

See how leading enterprises govern AI with Javelin -  request a demo 

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