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This is a Quantitative Study study.

October 16, 2025Open Access

Quantifying Conversation Drift in MCP via Latent Polytope

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Proposed framework SecMCP quantifies conversation drift in large language models, highlighting security risks.

SecMCP can detect conversation drift, enabling proactive measures against data exfiltration and misinformation.
The framework achieved AUROC scores exceeding 0.915 on benchmark datasets, indicating high detection performance.
By modeling QLLM activation vectors within a latent polytope space, SecMCP identifies anomalous conversational shifts.
Existing defenses are inadequate, yet SecMCP provides a systematic categorization of security threats within MCP.

Shi et al. (2025) studied this question.

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October 16, 2025Open Access

View Full Paper

Ask AI

Bookmark

Authors

Proposed framework SecMCP quantifies conversation drift in large language models, highlighting security risks.

SecMCP can detect conversation drift, enabling proactive measures against data exfiltration and misinformation.
The framework achieved AUROC scores exceeding 0.915 on benchmark datasets, indicating high detection performance.
By modeling QLLM activation vectors within a latent polytope space, SecMCP identifies anomalous conversational shifts.
Existing defenses are inadequate, yet SecMCP provides a systematic categorization of security threats within MCP.

Shi et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark