Content embeddings from SBERT enable AUROC above 0.89 for attack detection in MCP tool-call sessions, with tree ensembles on pooled embeddings reaching 0.975 and outperforming GNNs when using task-stratified splits instead of random ones.
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Content-Aware Attack Detection in LLM Agent Tool-Call Traffic: An Empirical Study of Features, Architectures, and Evaluation Protocols
Content embeddings from SBERT enable AUROC above 0.89 for attack detection in MCP tool-call sessions, with tree ensembles on pooled embeddings reaching 0.975 and outperforming GNNs when using task-stratified splits instead of random ones.