Introduction: Personal LLM Agents and Privacy Risks
LLMs are deployed as personal assistants, gaining access to sensitive user data through Personal LLM agents. This deployment raises concerns about contextual privacy understanding and the ability of these agents to determine when sharing specific user information is appropriate. Large reasoning models (LRMs) pose challenges as they operate through unstructured, opaque processes, making it unclear how sensitive information flows from input to output. LRMs utilize reasoning traces that make the privacy protection complex. Current research examines training-time memorization, privacy leakage, and contextual privacy in inference. However, they fail to analyze reasoning traces as explicit threat vectors in LRM-powered personal agents.
Related Work: Benchmarks and Frameworks for Contextual Privacy
Previous research addresses contextual privacy in LLMs through various methods. Contextual integrity frameworks define privacy as proper information flow within social contexts, leading to benchmarks such as DecodingTrust, AirGapAgent, CONFAIDE, PrivaCI, and CI-Bench that evaluate contextual adherence through structured prompts. PrivacyLens and AgentDAM simulate agentic tasks, but all target non-reasoning models. Test-time compute (TTC) enables structured reasoning at inference time, with LRMs like DeepSeek-R1 extending this capability through RL-training. However, safety concerns remain in reasoning models, as studies reveal that LRMs like DeepSeek-R1 produce reasoning traces containing harmful content despite safe final answers.
Research Contribution: Evaluating LRMs for Contextual Privacy
Researchers from Parameter Lab, University of Mannheim, Technical University of Darmstadt, NAVER AI Lab, the University of Tubingen, and Tubingen AI Center present the first comparison of LLMs and LRMs as personal agents, revealing that while LRMs surpass LLMs in utility, this advantage does not extend to privacy protection. The study has three main contributions addressing critical gaps in reasoning model evaluation. First, it establishes contextual privacy evaluation for LRMs using two benchmarks: AirGapAgent-R and AgentDAM. Second, it reveals reasoning traces as a new privacy attack surface, showing that LRMs treat their reasoning traces as private scratchpads. Third, it investigates the mechanisms underlying privacy leakage in reasoning models.
Methodology: Probing and Agentic Privacy Evaluation Settings
The research uses two settings to evaluate contextual privacy in reasoning models. The probing setting utilizes targeted, single-turn queries using AirGapAgent-R to test explicit privacy understanding based on the original authors’ public methodology, efficiently. The agentic setting utilizes the AgentDAM to evaluate implicit understanding of privacy across three domains: shopping, Reddit, and GitLab. Moreover, the evaluation uses 13 models ranging from 8B to over 600B parameters, grouped by family lineage. Models include vanilla LLMs, CoT-prompted vanilla models, and LRMs, with distilled variants like DeepSeek’s R1-based Llama and Qwen models. In probing, the model is asked to implement specific prompting techniques to maintain thinking within designated tags and anonymize sensitive data using placeholders.
Analysis: Types and Mechanisms of Privacy Leakage in LRMs
The research reveals diverse mechanisms of privacy leakage in LRMs through analysis of reasoning processes. The most prevalent category is wrong context understanding, accounting for 39.8% of cases, where models misinterpret task requirements or contextual norms. A significant subset involves relative sensitivity (15.6%), where models justify sharing information based on seen sensitivity rankings of different data fields. Good faith behavior is 10.9% of cases, where models assume disclosure is acceptable simply because someone requests information, even from external actors presumed trustworthy. Repeat reasoning occurs in 9.4% of instances, where internal thought sequences bleed into final answers, violating the intended separation between reasoning and response.
Conclusion: Balancing Utility and Privacy in Reasoning Models
In conclusion, researchers introduced the first study examining how LRMs handle contextual privacy in both probing and agentic settings. The findings reveal that increasing test-time compute budget improves privacy in final answers but enhances easily accessible reasoning processes that contain sensitive information. There is an urgent need for future mitigation and alignment strategies that protect both reasoning processes and final outputs. Moreover, the study is limited by its focus on open-source models and the use of probing setups instead of fully agentic configurations. However, these choices enable wider model coverage, ensure controlled experimentation, and promote transparency.
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