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MCP tool poisoning is an indirect prompt-injection attack in which malicious instructions are placed in a tool’s description, parameter schema, or returned content to influence an AI agent. The practical defense is layered: verify and pin reviewed tool definitions, restrict server permissions, enforce authorization outside the model, treat tool output as untrusted data, and require confirmation for consequential actions.

What MCP tool poisoning means

The OWASP Cheat Sheet Series defines tool poisoning as “Malicious instructions hidden in tool descriptions, parameter schemas, or return values that manipulate the LLM’s behavior.” In plain terms, the agent encounters attacker-controlled text through a tool interface or result, and may treat that text as an instruction rather than data.

MCP connects AI applications to external tools and data sources. That makes a server’s metadata and responses a trust boundary: an agent might read a tool description while deciding what to call, or consume a result after a call. If the agent also has access to sensitive data or other powerful tools, poisoned text could try to steer it toward an unintended action. Whether that succeeds depends on the client, available tools, permissions, validation, and approval controls; the attack is not automatic.

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How the attack can reach an agent

Instructions in metadata

A malicious or compromised server can put instructions in a tool description or parameter schema. A tool’s benign-looking name and short summary do not establish that its full description, schema, server implementation, or behavior is safe. Review the complete interface and the source and privileges of the server.

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Instructions in runtime results

A tool can return text that attempts to redirect the agent after execution. Treat the result as untrusted data, even when it comes from a tool the agent was permitted to call. Validation can check structure or constrain what enters the model’s context, but it cannot prove that arbitrary natural-language content is harmless.

Related risks: shadowing and rug pulls

OWASP distinguishes related patterns. Tool shadowing or cross-origin escalation occurs when a malicious server’s description influences the agent’s use of tools from other servers. A rug pull occurs when a server changes its tool definitions after approval. Both exploit trust in the tool boundary, but they are not simply synonyms for every prompt injection.

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Which defenses address which boundary?

Control What it helps address What it does not establish
Server vetting and allowlisting Reduces exposure to unapproved or poorly understood servers by checking the publisher, source, interface, and requested permissions. Approval does not guarantee that a server’s code or behavior remains safe.
Reviewed-definition pinning Detects changes to tool metadata and can trigger renewed review or consent. A matching hash covers the pinned metadata, not the integrity of server code or behavior behind an unchanged definition.
Least privilege and isolation Limits what a compromised or manipulated tool interaction can reach, reducing potential blast radius. Does not itself identify malicious instructions or make a server trustworthy.
Execution-layer authorization and validation Checks whether a requested operation and its arguments are permitted before execution; constrains what results enter model context. Schema-valid input or output is not necessarily safe or semantically benign.
Explicit user confirmation Creates a human decision point before destructive, financial, or data-sharing actions. Does not replace authorization checks or guarantee the user will recognize a deceptive request.
Monitoring and response Helps detect unexpected calls or metadata changes and supports blocking or quarantining suspicious tools. Logging is detection and response, not a preventive access-control boundary.

How to reduce the risk in practice

  1. Approve servers deliberately. Verify the publisher and source, then review the complete names, descriptions, parameter schemas, and permissions. Allowlist only servers that have an understood purpose. Anthropic’s MCP Directory Policy says descriptions must “narrowly and unambiguously describe what the tool does and when they should be invoked,” and should match actual functionality.
  2. Record the reviewed interface and detect changes. Save the tool definitions that were examined and compare them when they change. Require a fresh review or consent after material changes. A hash can flag metadata changes, but it is not proof that unchanged metadata points to safe code.
  3. Reduce permissions and separate trust domains. Give each server only the access it needs. Use scoped credentials, sandbox local servers, and keep high-risk tools separate from untrusted external servers. Do not assume a model instruction such as “never reveal secrets” is an access-control mechanism.
  4. Enforce policy in trusted execution code. Check authorization at the point where the tool runs, validate arguments before execution, and validate or constrain outputs before putting them into model context. Treat returned content as data, not as a new source of policy.
  5. Put a user gate in front of consequential actions. Before destructive, financial, or data-sharing operations, show the full parameters and require explicit confirmation. The confirmation should be attached to the actual operation the user is approving.
  6. Log and respond to suspicious activity. Record security-relevant invocations and metadata changes, alert on unexpected behavior, and block or quarantine tools suspected of poisoning or tampering. Protect logs from retaining unnecessary secrets or personal data.
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What these controls can—and cannot—promise

No description scanner or text filter can establish that all remaining content is safe. A schema can reject malformed data but cannot prove that well-formed text is benign. A metadata hash detects changes within its scope, not hidden changes in implementation. These controls are most useful in combination: reduce the chance of trusting hostile content, limit what the agent can do if it is influenced, and put authorization and human review at the actions that matter.

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