AI Interview Series #2: Explain Some of the Common Model Context Protocol (MCP) Security Vulnerabilities
In this half of the Interview Series, we’ll have a look at some of the frequent safety vulnerabilities in the Model Context Protocol (MCP) — a framework designed to let LLMs safely work together with exterior instruments and knowledge sources. While MCP brings construction and transparency to how fashions entry context, it additionally introduces new safety dangers if not correctly managed. In this text, we’ll discover three key threats — MCP Tool Poisoning, Rug Pulls, and Tool Hijacking Attacks
Tool Poisoning
A Tool Poisoning Attack occurs when an attacker inserts hidden malicious directions inside an MCP instrument’s metadata or description.
- Users solely see a clear, simplified instrument description in the UI.
- LLMs, nonetheless, see the full instrument definition — together with hidden prompts, backdoor instructions, or manipulated directions.
- This mismatch permits attackers to silently affect the AI into dangerous or unauthorized actions.
Tool Hijacking
A Tool Hijacking Attack occurs while you join a number of MCP servers to the similar shopper, and one of them is malicious. The malicious server injects hidden directions inside its personal instrument descriptions that attempt to redirect, override, or manipulate the conduct of instruments supplied by a trusted server.
In this case, Server B pretends to supply a innocent add() instrument, however its hidden directions attempt to hijack the email_sender instrument uncovered by Server A.
MCP Rug Pulls
An MCP Rug Pull occurs when a server adjustments its instrument definitions after the person has already accredited them. It’s just like putting in a trusted app that later updates itself into malware — the shopper believes the instrument is secure, however its conduct has silently modified behind the scenes.
Because customers hardly ever re-review instrument specs, this assault is extraordinarily onerous to detect.
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