MCP Security: the new attack chain targeting AI Tools

TL;DR

The Model Context Protocol (MCP) enables AI systems to interact with internal tools and data, but it also creates a new attack surface. Attackers can exploit MCP through prompt injection, tool poisoning, and over-privileged access to execute unauthorized actions or exfiltrate sensitive data. As AI agents become more autonomous, MCP security testing and AI systems pentesting are becoming critical to protect organizations from this new attack chain.

What is the Model Context Protocol and why it changes the threat landscape

The Model Context Protocol (MCP) is an emerging standard designed to connect AI models with external tools, systems, and data sources. It allows AI assistants and agents to perform actions such as querying databases, accessing files, and executing workflows.

While this increases productivity and automation, it also fundamentally changes how security needs to be approached. AI systems are no longer passive interfaces but active participants capable of making decisions and triggering actions.

This shift introduces new risks because attackers can manipulate context, tools, and permissions rather than just exploiting software vulnerabilities.

Why MCP creates a completely new attack chain

Traditional cyberattacks target applications, infrastructure, or users. MCP environments introduce a new layer where the AI agent itself becomes a potential attack vector.

Because MCP integrates multiple systems simultaneously, a compromise can propagate quickly across tools and workflows. Attackers can exploit trust relationships between agents and tools to execute actions that appear legitimate.

This expands the attack surface beyond code and networks to include context, decision logic, and tool orchestration.

Key MCP security risks

Prompt injection via tools

Attackers can craft malicious input that manipulates an AI agent into performing unintended actions or revealing sensitive information.

Because the AI interprets context dynamically, these attacks can bypass traditional input validation mechanisms.

Tool poisoning

MCP relies on tool descriptions and metadata. Attackers can embed malicious instructions in these descriptions to influence how an AI agent behaves.

This can lead to unauthorized actions or data exfiltration without triggering traditional security alerts.

Over-privileged access

If AI agents have excessive permissions, a compromise can lead to large-scale impact. Attackers may gain access to internal systems through the agent’s elevated privileges.

Credential and token theft

MCP servers often rely on API keys and tokens to connect tools. If compromised, attackers can gain access to multiple services simultaneously.

Supply chain risks

Because MCP integrates third-party tools, a compromised integration can introduce vulnerabilities into the entire environment.

From vulnerability to exploit: how an MCP attack unfolds

A typical MCP attack chain may look like this:

1. An attacker injects malicious input or tool metadata
2. The AI agent calls a tool with elevated privileges
3. Sensitive data is exposed or unauthorized actions are executed
4. The activity appears legitimate and bypasses traditional monitoring

This scenario is often described as a “confused deputy” problem, where the AI acts with more authority than the user.

Why traditional security controls are no longer sufficient

Most traditional security models assume predictable systems and clear user actions.

AI agents, however, make autonomous decisions and combine context from multiple sources. This introduces risks such as:

• unpredictable behavior
• context leakage
• chained exploits
• real-time privilege misuse

Organizations therefore need new testing approaches that focus on behavior and interactions rather than only code vulnerabilities.

MCP and the rise of agent compromise

One of the biggest emerging risks in AI security is the compromise of autonomous agents.

When agents have access to data and operational tools, attackers can manipulate them to execute unauthorized actions or leak sensitive information.

This makes agent security a critical component of modern cybersecurity strategies.

How organizations can protect against MCP risks

An effective approach combines governance, identity controls, and specialized testing.

Map AI and agent integrations

Identify all MCP servers, tools, and connectors in your environment.

Apply least privilege

Limit permissions and implement just-in-time access.

Monitor behavior

Detect abnormal tool calls and context usage.

Test AI workflows

Simulate attacks such as prompt injection and tool misuse.

Why AI systems pentesting is becoming essential

Just as application pentesting became standard for software security, AI systems testing is becoming essential for organizations using agentic workflows.

Testing AI workflows and tool integrations helps identify risks before they can be exploited.

If you want insight into how vulnerable your AI integrations are and what attack paths exist, explore our approach to AI systems security testing.

MCP, AI security and compliance

Regulations such as the EU AI Act and NIS2 increasingly require organizations to manage AI risks and integrations.

Because MCP provides direct access to data and systems, testing agent workflows will become a key part of compliance and audit readiness.

The future of cybersecurity: from securing systems to controlling behavior

As agentic AI continues to evolve, cybersecurity will shift from protecting infrastructure to governing behavior and trust.

Future security strategies will focus on:

• identity and trust models
• monitoring autonomous decisions
• governance of AI ecosystems
• continuous validation

Organizations that adapt to this shift will be better prepared for the next generation of cyber threats.

Conclusion

The Model Context Protocol enables powerful AI integrations but introduces a new attack chain that many organizations still underestimate.

Prompt injection, tool poisoning, and privilege escalation demonstrate that AI agents are a new attack surface that goes beyond traditional security models.

By actively testing AI systems and agent workflows, organizations can identify risks early and build resilience in an environment where AI not only responds but acts.

Frequently Asked Questions

What is MCP in cybersecurity

The Model Context Protocol is a standard that connects AI models with external tools and systems.

Why is MCP a security risk

Because AI agents can perform actions with access to systems and data, misuse can have significant impact.

What is tool poisoning

An attack where malicious instructions are embedded in tool metadata.

How do you secure AI agents

Through identity controls, monitoring, and specialized AI security testing.