Machine Learning-Based Detection of MCP Attacks
The Model Context Protocol (MCP) is a new and emerging technology that extends the functionality of large language models, improving workflows but also exposing users to a new attack surface. Several studies have highlighted related security flaws, but MCP attack detection remains underexplored. To address this research gap, this study develops and evaluates a range of supervised machine learning approaches, including both traditional and deep-learning models. We evaluated the systems on the detection of malicious MCP tool descriptions in two scenarios: (1) a binary classification task distinguishing malicious from benign tools, and (2) a multiclass classification task identifying the att...
| Authors | Tobias Mattsson, Samuel Nyberg, Anton Borg et al. |
|---|---|
| Primary category | cs.CR |
| Published | 12 April 2026 |
| Last updated at source | 12 April 2026 |
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