As artificial intelligence becomes increasingly integrated into business processes, risks are also on the rise. Today, your AI agents don’t just interact with you: they connect to databases, APIs, and external systems. This opens new doors for attackers who, if you are unprepared, can turn your tools into a weak point.
At TecnetOne, we want to help you understand the main risks MCP (Model Context Protocol) servers face and the measures you can take to mitigate them.
What it is: when an attacker modifies the code of an AI tool to steal data or alter its behavior.
The danger is that the tool appears to work fine on the surface but may secretly leak information or manipulate results. This attack often occurs during development or via the supply chain (for example, through infected libraries).
How to defend yourself:
Verify code integrity before going live.
Perform periodic security audits, especially after updates.
Test tools in isolated environments (sandbox).
Use version control with approval workflows.
Monitor production for anomalous behavior.
What it is: malicious inputs designed to trick the AI into ignoring original instructions.
Attackers embed commands in text, URLs, documents, or even images, causing the model to execute unauthorized actions — from leaking internal data to bypassing security rules.
How to defend yourself:
Clean and validate all input before it reaches the model.
Separate user data from system instructions.
Use clear, structured prompt templates.
Implement filters to detect malicious patterns.
Limit input length and allowed characters.
What it is: creating fake tools that mimic the name or interface of legitimate ones.
This tricks the system into using the fake tool instead of the real one, intercepting sensitive data or delivering manipulated results.
How to defend yourself:
Enforce strict naming and identity verification rules.
Maintain approved lists reviewed periodically.
Require security reviews before adding new tools.
Implement version controls with integrity validation.
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What it is: when a seemingly legitimate tool copies and sends sensitive information to third parties.
The risk is high because users often share business documents assuming safety, while a malicious tool may quietly collect data from multiple conversations.
How to defend yourself:
Monitor tool outputs for unusual patterns.
Set data volume limits per operation.
Scan outputs for sensitive info before delivering to users.
Audit logs and access frequently.
Control outgoing traffic to external domains.
What it is: when an attacker triggers internal tools they should not have access to.
This can occur by manipulating prompts or exploiting APIs. Once inside, attackers may chain multiple invocations to escalate privileges.
How to defend yourself:
Define strict role-based permissions.
Apply usage limits and anomaly detection.
Log every invocation with details (who, when, what).
Validate that calls come from authorized users.
What it is: abusing tools with excessive privileges, capable of accessing all data or critical settings.
If attackers compromise one of these, the impact can be devastating.
How to defend yourself:
Apply the least privilege principle.
Split critical functions into smaller, controlled tools.
Require MFA or extra approvals for sensitive actions.
Audit privileged tool usage regularly.
What it is: passwords, API keys, or tokens stored in conversation history or exposed unintentionally.
This happens when developers paste logs with credentials or when a tool returns overly detailed data.
How to defend yourself:
Educate users not to share credentials in chats.
Automate detection and removal of sensitive data in logs.
Use dynamic credentials with frequent rotation.
Store secrets only in secure vaults, never in plaintext.
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What it is: attackers manipulate parameters passed to tools to execute arbitrary code on the system.
This occurs if inputs are not validated properly and end up forming system commands.
How to defend yourself:
Enforce strict parameter validation.
Escape special characters.
Run execution in containers with minimal privileges.
Monitor system calls for abnormal activity.
What it is: tools with far broader permissions than necessary on external services.
Attackers exploit this to read sensitive data or change critical settings.
How to defend yourself:
Use granular permissions, never default admin access.
Rotate API keys regularly; use short-lived tokens.
Monitor for abnormal usage patterns.
Apply rate limiting.
Use separate keys for each tool.
What it is: attackers combine multiple legitimate tools in unintended sequences to gain unauthorized access.
Each call may be legitimate, but the final result is an attack.
How to defend yourself:
Validate full workflows, not just individual tools.
Limit chain depth.
Isolate contexts between tool calls.
Implement “circuit breakers” to stop suspicious sequences.
There’s no single solution to cover all risks. Securing your MCP servers and AI tools requires a defense-in-depth strategy combining:
Least privilege principle
Rigorous input validation
Continuous monitoring of behavior
Regular audits of code, permissions, and logs
Multiple layered controls instead of relying on one alone
At TecnetOne, we’re clear: as AI grows in power, so do those seeking to exploit it. Preparing today with proactive controls can mean the difference between a contained incident and a devastating breach.