Invariant Labs
A company focused on security research for AI systems, known for naming and analyzing attack techniques targeting AI agents, including MCP tool poisoning and related threats in agentic AI deployments.
Core Facts
Timeline (last 90 days)
Invariant Labs demonstrated a tool poisoning attack: hidden instructions in the tool description of a "daily fun fact" MCP server manipulated the model into extracting WhatsApp history and sending it to the attacker.
In April 2025, Invariant Labs conducted a proof-of-concept experiment demonstrating a complete attack chain that steals users' chat records through hidden instructions in a malicious MCP server.
In the proof of concept, the attacker wrote instructions into the hidden notes of a trivia plugin, requiring the AI to also send the chat history to a specified phone number when sending a WhatsApp message; the user only asked "help me message my mom that I'll be late," and the AI carried out the data exfiltration.
In this attack scenario, the approval interface hid most of the content of the second message, and the confirmation screen the user saw looked completely normal, so the user could approve the data exfiltration without knowing it.
The Invariant Labs report notes that such attacks do not require the user to actively invoke the malicious tool—it can be triggered as long as the tool exists in the context—making it harder to defend against than traditional prompt injection.
According to Invariant Labs' research and a YouTube channel's analysis, this hidden-instruction vulnerability is not an isolated case but a design challenge普遍 present in the MCP architecture.
In April 2025, researchers at Invariant Labs demonstrated that tool descriptions can carry instructions and be executed by AI; in the test, an attacker-controlled server, through hidden instructions, sent chat records to the attacker's number at the same time the user sent a WhatsApp message.
In May 2025, Invariant Labs researchers connected an AI Agent to the official GitHub MCP server; the attacker hid instructions in a public project Issue, and after the Agent read them it executed the instructions and copied sensitive information such as salaries from a private project to a public page.
Invariant Labs在2025年4月1日首次将描述中夹带客户端界面不可见指令的现象命名为'工具投毒'（tool poisoning）。
Invariant Labs demonstrated an attack against the official GitHub MCP server: after planting a malicious issue in a public repository, the agent pulled data from the repository owner's private repositories and published it to a public pull request.
2 more timeline events
All Facts (12)
Invariant Labs demonstrated an attack against the official GitHub MCP server: after planting a malicious issue in a public repository, the agent pulled data from the repository owner's private repositories and published it to a public pull request.
65%UnverifiedAccording to Invariant Labs' research and a YouTube channel's analysis, this hidden-instruction vulnerability is not an isolated case but a design challenge普遍 present in the MCP architecture.
50%UnverifiedIn this attack scenario, the approval interface hid most of the content of the second message, and the confirmation screen the user saw looked completely normal, so the user could approve the data exfiltration without knowing it.
50%UnverifiedThe Invariant Labs report notes that such attacks do not require the user to actively invoke the malicious tool—it can be triggered as long as the tool exists in the context—making it harder to defend against than traditional prompt injection.
50%UnverifiedInvariant Labs在2025年4月1日首次将描述中夹带客户端界面不可见指令的现象命名为'工具投毒'（tool poisoning）。
50%UnverifiedInvariant Labs pointed out that there is no bug in the GitHub MCP server code itself — it is the combination of capabilities that constitutes the vulnerability.
50%UnverifiedInvariant Labs demonstrated a tool poisoning attack: hidden instructions in the tool description of a "daily fun fact" MCP server manipulated the model into extracting WhatsApp history and sending it to the attacker.
50%UnverifiedIn April 2025, Invariant Labs conducted a proof-of-concept experiment demonstrating a complete attack chain that steals users' chat records through hidden instructions in a malicious MCP server.
50%UnverifiedIn the proof of concept, the attacker wrote instructions into the hidden notes of a trivia plugin, requiring the AI to also send the chat history to a specified phone number when sending a WhatsApp message; the user only asked "help me message my mom that I'll be late," and the AI carried out the data exfiltration.
50%UnverifiedIn May 2025, Invariant Labs researchers connected an AI Agent to the official GitHub MCP server; the attacker hid instructions in a public project Issue, and after the Agent read them it executed the instructions and copied sensitive information such as salaries from a private project to a public page.
50%UnverifiedIn April 2025, researchers at Invariant Labs demonstrated that tool descriptions can carry instructions and be executed by AI; in the test, an attacker-controlled server, through hidden instructions, sent chat records to the attacker's number at the same time the user sent a WhatsApp message.
50%UnverifiedInvariant Labs coined the term for MCP tool poisoning attacks in April 2025.
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