Conference & Journal Paper
HelpBench: Assessing the Ability of LLMs to Provide Privacy, Safety, and Security Advice
Sarah Meiklejohn, Sunny Consolvo, Patrick Gage Kelley, Tara Matthews, Sai Teja Peddinti, Renee Shelby, Lenin Simicich, Kurt Thomas
arXiv preprint arXiv:2606.24819, 2026
June 2026
Abstract
This paper introduces HelpBench, a benchmark for assessing whether LLMs are capable of providing accurate help in response to questions about digital privacy, safety, and security. We curated 450 questions representing authentic user situations and developed rubrics for each question to evaluate the factual accuracy and tone of a response. Example questions touch on how to regain access to lost or suspended accounts, how to balance the trade-offs of hardware security keys versus other forms of two-factor authentication, whether a suspicious email is likely a scam, or whether an abuser might be able to track an individual based on their device peripherals. We then developed and applied an auto-rater to evaluate responses from 18 state-of-the-art LLMs. Our results indicate that while models provide high-quality advice (with scores of 82% on average), one in ten responses from models scores less than 65%, reflecting inaccurate and even harmful advice. Addressing these failures is critical for models to serve as trustworthy sources of assistance for digital privacy, safety, and security needs.
- help-seeking
- privacy
- security
- generative ai
- ai
- benchmark
Cite this paper
@misc{meiklejohn2026helpbench,
title = {HelpBench: Assessing the Ability of LLMs to Provide Privacy, Safety, and Security Advice},
author = {Meiklejohn, Sarah and Consolvo, Sunny and Kelley, Patrick Gage and Matthews, Tara and Peddinti, Sai Teja and Shelby, Renee and Simicich, Lenin and Thomas, Kurt},
year = {2026},
eprint = {2606.24819},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.24819},
}