Prevalence of Security and Privacy Risk-Inducing Usage of AI-based Conversational Agents

TL;DR

This study surveys UK adults to assess security and privacy risks associated with AI conversational agents, revealing widespread risky behaviors and lack of user awareness about data privacy and model training implications.

Contribution

It provides empirical data on user behaviors and awareness regarding security risks in AI conversational agents, highlighting the need for improved safeguards and transparency.

Findings

Up to one-third of regular users exhibit behaviors enabling attacks

One-fourth of users have attempted jailbreaking of AI models

Most users are unaware of data training and opt-out options

Abstract

Recent improvement gains in large language models (LLMs) have lead to everyday usage of AI-based Conversational Agents (CAs). At the same time, LLMs are vulnerable to an array of threats, including jailbreaks and, for example, causing remote code execution when fed specific inputs. As a result, users may unintentionally introduce risks, for example, by uploading malicious files or disclosing sensitive information. However, the extent to which such user behaviors occur and thus potentially facilitate exploits remains largely unclear. To shed light on this issue, we surveyed a representative sample of 3,270 UK adults in 2024 using Prolific. A third of these use CA services such as ChatGPT or Gemini at least once a week. Of these ``regular users'', up to a third exhibited behaviors that may enable attacks, and a fourth have tried jailbreaking (often out of understandable reasons such as curiosity, fun or information seeking). Half state that they sanitize data and most participants report not sharing sensitive data. However, few share very sensitive data such as passwords. The majority are unaware that their data can be used to train models and that they can opt-out. Our findings suggest that current academic threat models manifest in the wild, and mitigations or guidelines for the secure usage of CAs should be developed. In areas critical to security and privacy, CAs must be equipped with effective AI guardrails to prevent, for example, revealing sensitive information to curious employees. Vendors need to increase efforts to prevent the entry of sensitive data, and to create transparency with regard to data usage policies and settings.