arXiv:2610.40890v1 Announce Type: new Abstract: Large language models (LLMs) can generate detailed guidance, functional code, and persuasive text at scale. As one of the most widely used consumer language model products as of 2026, ChatGPT gives broad public access to these capabilities, motivating concerns about its potential use in surveillance-related tasks. We present an exploratory case study of what potentially harmful surveillance assistance looks like in ChatGPT's public web interface. We contribute a taxonomy of seven surveillance risk categories based on prior LLM risk research and reports of deployed surveillance systems, instantiated as 110 prompts, along with a manually reviewed analysis of ChatGPT's responses. Prompts were issued using two approaches: 55 jailbreak prompts drawing on five types of elicitation strategies, and 55 plain-language direct prompts, each run alone, and if unsuccessful, again with a short fixed prefix. We find that ChatGPT provides substantive surveillance guidance for 40 of the 55 jailbreak prompts and for 33 of the 55 direct prompts without any prefix. Compliant responses also rarely included policy mentions or suggestions for privacy-preserving alternatives. We manually review every response and find that even when ChatGPT states a refusal, it may still supply detailed instructions, working code, or architectural guidance alongside brief ethical caveats.
[AINews] LLMs can read & write neural network weights: "Weight Space Programming" cuts Autoresearch out of the RSI loop