arXiv:2602.24210v2 Announce Type: replace-cross Abstract: Large reasoning models (LRMs) produce reasoning traces (RTs) that often contain sensitive information. These leaky thoughts are difficult to control and frequently violate explicit privacy directives. Because RTs can be exposed through prompt injection attacks, this becomes a direct privacy risk to the user. We approach this as a controllability problem: since privacy directives are themselves instructions, improving instruction-following (IF) within the RT provides a direct path to reducing privacy leaks. To this end, we introduce an S
The proliferation of large reasoning models and prompt injection attacks highlights the immediate need for robust privacy controls within AI's internal processes.
Controlling 'leaky thoughts' in AI models is crucial for ensuring privacy, maintaining trust, and preventing sensitive data exposure, especially as AI integrates into critical systems.
The focus is shifting from external data privacy to internal AI reasoning privacy, introducing new instruction-following challenges and potential solutions for secure AI deployment.
- · AI researchers focusing on privacy and controllability
- · Organizations deploying AI in sensitive environments
- · Users concerned about data privacy
- · Malicious actors exploiting prompt injection
- · Less secure AI models without internal privacy controls
- · Existing data privacy frameworks that only address external interactions
Improved instruction-following in large reasoning models will directly reduce privacy leaks from internal thought processes.
Enhanced privacy controls within AI models could accelerate their adoption in highly regulated sectors like healthcare and finance.
The development of 'private reasoning' could lead to new standards for AI safety and ethics, influencing future regulatory frameworks globally.
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