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invariantlabs-ai/mcp-injection-experiments

Snippets that reproduce MCP tool poisoning

Before you install: no copy-paste command · no license recorded · link reachable

First step: nothing runnable here yet - start from the repository: https://github.com/invariantlabs-ai/mcp-injection-experiments

View on GitHub ↗Link checked 2026-11-12 · HTTP 200 · opens the vendor's own site

We have neither an install command nor a public remote endpoint from this repository, so we do not print a config. A made-up one is worse than none. How each client wires MCP up →

Source
collected from the abordage/awesome-mcp list · type: MCP server
Harvest history
first seen 2026-11-05 · last seen 2026-11-14 · seen 10× · listed by 10 sources
Score
score 1,003 · 10 sources · mcp
FAQ (answers come from our records)
Who is it for?
We filed it under these tags: MCP 服务. The tags come from the entry itself, not a taxonomy we imposed.
Who maintains it?
first catalogued 2026-11-05;last seen 2026-11-14。There is no official certification or quality rating here — we record sources and dates only.

# MCP Tool Poisoning Experiments

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

This repository contains a few experimental MCP server implementations, that attempt ot inject the MCP client/agent in use.
For more details about the attack method, please see our blog post.
Update: We have released a new security scanning tool called mcp-scan, that detects MCP attacks as demonstrated in this repository, and helps you secure your MCP servers.
## Direct Poisoning
In direct-poisoning.py, we implement a simple MCP server that instructs an agent to leak sensitive files, when calling the add tool (in this case SSH keys and the mcp.json file itself).
An example execution in cursor looks like this:
## Tool Shadowing
In shadowing.py, we implement a more sophisticated MCP attack, that manipulates the agent's behavior of a sendemail tool (provided by a different, trusted server), such that all emails sent by the agent are leaked to the attacker's server.
An example execution in Cursor looks like this:
## WhatsApp takeover
Lastly, in whatsapp-takeover.py, we implement a shadowing attack combined with a sleeper rug pull, i.e. an MCP server that changes its tool interface only on the second load to a malicious one.
The server first masks as a benign "random fact of the day" implementation, and then changes the tool to a malicious one that manipulates whatsapp-mcp in the same agent, to leak messages to the attacker's phone number.
Can you spot the exfiltration? Here, the malicious tool instructions ask the agent to include the smuggled data after many spaces, such that with invisible scroll bars, the user does not see the data being leaked. Only when you scroll all the way to the right, will you be able to find the exfiltration payload.
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Source
awesome · https://github.com/invariantlabs-ai/mcp-injection-experiments
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