arXiv:2606.18062v1 Announce Type: new Abstract: Large language models (LLMs) are widely used to fulfill users' information needs; users ask LLMs about the weather, pose educational questions, and consult them for legal assistance. One particularly understudied area is digital security and privacy (S&P), where users may seek LLMs' help on how to secure their online accounts or protect their computers from cyber attacks. To the best of our knowledge, no prior study has collected or analyzed the S&P questions users ask LLMs; prior research on LLM response quality relied on expert-authored S&P mis
The proliferation of LLMs into everyday life necessitates understanding how users interact with them for critical information, particularly concerning security and privacy, which has been an understudied area.
This research reveals critical user behaviors and potential vulnerabilities or strengths of LLMs in delivering security and privacy advice, impacting trust and safety in AI deployment.
We gain insight into the actual 'in the wild' prompts users employ for security and privacy, allowing for better model training, moderation, and policy development around AI assistance in these sensitive domains.
- · AI developers
- · Cybersecurity researchers
- · Users seeking security advice
- · Malicious actors exploiting LLM vulnerabilities
- · Companies with insecure LLM deployments
Increased understanding of user security queries to LLMs.
Improved security and privacy features, moderation, and guardrails in future LLM versions.
Enhanced overall digital security posture for a wider user base due to more reliable AI-driven advice.
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