Defenses at Odds: Measuring and Explaining Defense Conflicts in Large Language Models

Name: Defenses at Odds: Measuring and Explaining Defense Conflicts in Large Language Models
Uploaded: 2026-10-02T01:53:10.960561+00:00
Duration: 441 s
Description: This study systematically tested how stacking different AI defenses sequentially affects safety, privacy, and fairness risks in LLMs. It found that defenses often clash, leading to measurable risk increases in 39% of cases. The research identifies structural causes for these conflicts and proposes a method to prevent them by freezing specific model layers.

This study systematically tested how stacking different AI defenses sequentially affects safety, privacy, and fairness risks in LLMs. It found that defenses often clash, leading to measurable risk increases in 39% of cases. The research identifies structural causes for these conflicts and proposes a method to prevent them by freezing specific model layers.

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