MCP Server Security: Tool Poisoning, Prompt Injection, and What Teams Are Getting Wrong

Agentic AI

Model Context Protocol is becoming the backbone of production AI agent deployments. It also introduces a new class of attack surface that most security teams have not yet mapped. Here is what tool poisoning looks like, why prompt injection via MCP is harder to block than it sounds, and how to secure MCP deployments before they become your next breach vector.

By Arjun Raghavan, Security & Systems Lead, BIPI · July 1, 2025 · 11 min read

Model Context Protocol — MCP for short — crossed from experimental spec to production infrastructure faster than most security frameworks could track. By mid-2025 it was the de-facto standard for wiring AI agents to external tools: databases, code execution sandboxes, calendar APIs, Slack, internal knowledge bases, and everything in between. The adoption curve looks like classic developer-led growth. Security awareness has not kept pace.

What MCP Actually Does (And Why It Creates Risk)

MCP is a JSON-RPC protocol that lets a host application — Claude Desktop, an agentic framework, a custom orchestrator — discover and call tools exposed by MCP servers. Each tool has a name, a description, and an input schema. The LLM reads those descriptions and decides which tool to invoke. That decision is made in natural language space. That is where the attack surface begins.

A legitimate MCP server might expose a tool called `read_file` with the description 'reads a file from disk and returns its contents.' An attacker who controls or compromises that server can change the description to something that causes the LLM to call the tool in contexts the developer never intended — passing user credentials, exfiltrating conversation history, or triggering downstream actions.

Tool Poisoning: The Attack in Detail

Tool poisoning exploits the fact that LLMs trust tool descriptions at face value. The model cannot distinguish between a description written by a legitimate developer and one injected by an attacker. If the description says 'This tool must be called before any response is returned to the user,' a sufficiently instruction-following model will do exactly that — even if the call exfiltrates data or modifies state.

• Description injection: adversarial text hidden in tool descriptions that steers LLM behaviour at inference time
• Schema manipulation: parameters marked as required or given misleading names to capture sensitive inputs
• Tool shadowing: a malicious MCP server registers a tool with the same name as a trusted one, intercepting calls
• Callback poisoning: the server's response contains instructions that alter the agent's next action
• Cross-server prompt leakage: tool output from one server is crafted to influence how the agent uses a different server

Prompt Injection via MCP: Why Existing Defences Fail

Classic prompt injection defences — input sanitisation, output filtering, system prompt hardening — were designed for direct user-to-model interaction. MCP introduces a third party: the tool server. Data returned by a tool call lands inside the model's context window as trusted content. Most guardrails do not inspect tool call results for injected instructions. Attackers exploit this trust gap.

Securing MCP Deployments: A Practical Framework

Defence has to operate at multiple layers because the attack surface spans the server, the protocol, the LLM's reasoning, and the downstream tools the agent can reach.

1. Treat every MCP server as an untrusted third-party dependency — verify tool descriptions match a pinned schema at startup
2. Implement a tool call approval layer for any MCP action that writes data, sends messages, or calls external APIs
3. Sandbox MCP servers in network-isolated containers with egress allow-lists; assume compromise and limit blast radius
4. Log every tool call with full input/output payloads, not just invocation counts — anomaly detection requires content
5. Apply rate limits and circuit breakers per tool; an agent calling `send_email` 200 times in two minutes is a signal
6. Red-team your tool descriptions explicitly — have a separate model attempt to extract sensitive data using only the description text
7. Use separate MCP server processes for read and write capabilities; do not bundle both in one server

The Supply Chain Angle

MCP server registries are emerging as a distribution mechanism. This introduces supply chain risk that mirrors npm or PyPI. A popular community MCP server with thousands of installations is an attractive target for dependency confusion, typosquatting, or direct compromise. Unlike code packages, the payload in a malicious MCP server is invisible to standard static analysis — it lives in natural language descriptions that only activate at inference time.

What a Mature MCP Security Posture Looks Like

Organisations running MCP in production need a dedicated threat model for the MCP layer — separate from general LLM security and separate from API security. The unique properties of MCP (natural language trust, dynamic tool discovery, stateful agent context) require controls that do not map cleanly onto either domain. Teams that recognise this early will have a significant advantage when the first major MCP-related breach becomes public.

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