# Focused on the User, Overlooking the Risks: Security and Privacy Understandings, Practices and Challenges of Independent Chinese AI Agent Developers

arXiv: https://arxiv.org/abs/2606.03190 · HTML: https://mlsanity.com/paper/2606.03190/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 16 of its 111 references that are in the map.
- First posted on arXiv 2 Jun 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 16 of 16)

- [Large Language Model-Brained GUI Agents: A Survey](https://mlsanity.com/paper/2411.18279/) (2024)
- [Adversarial Machine Learning -- Industry Perspectives](https://mlsanity.com/paper/2002.05646/) (2020)
- [A Survey of WebAgents: Towards Next-Generation AI Agents for Web Automation with Large Foundation Models](https://mlsanity.com/paper/2503.23350/) (2025)
- ["It's a Fair Game", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents](https://mlsanity.com/paper/2309.11653/) (2023)
- [Understanding Users' Security and Privacy Concerns and Attitudes Towards Conversational AI Platforms](https://mlsanity.com/paper/2504.06552/) (2025)
- [PrivWeb: Unobtrusive and Content-aware Privacy Protection For Web Agents](https://mlsanity.com/paper/2509.11939/) (2025)
- [Development in times of hype: How freelancers explore Generative AI?](https://mlsanity.com/paper/2401.05790/) (2024)
- [Adanonymizer: Interactively Navigating and Balancing the Duality of Privacy and Output Performance in Human-LLM Interaction](https://mlsanity.com/paper/2410.15044/) (2024)
- [Understanding Users' Privacy Perceptions Towards LLM's RAG-based Memory](https://mlsanity.com/paper/2508.07664/) (2025)
- [Cooperation in the Gig Economy: Insights from Upwork Freelancers](https://mlsanity.com/paper/2301.08808/) (2023)
- [Compliance Requirements in Large-Scale Software Development: An Industrial Case Study](https://mlsanity.com/paper/2103.01821/) (2021)
- ["We are not Future-ready": Understanding AI Privacy Risks and Existing Mitigation Strategies from the Perspective of AI Developers in Europe](https://mlsanity.com/paper/2510.00909/) (2025)
- [Security in Online Freelance Software Development: A case for Distributed Security Responsibility](https://mlsanity.com/paper/2307.06066/) (2023)
- [Defying the Odds: Solana's Unexpected Resilience in Spite of the Security Challenges Faced by Developers](https://mlsanity.com/paper/2406.13599/) (2024)
- ["Sorry for bugging you so much." Exploring Developers' Behavior Towards Privacy-Compliant Implementation](https://mlsanity.com/paper/2504.06697/) (2025)
- [Privacy Perspectives and Practices of Chinese Smart Home Product Teams](https://mlsanity.com/paper/2506.06591/) (2025)

## Abstract (arXiv)

The proliferation of AI agents empowers independent developers, defined as individual or small groups who self-initiate projects rather than fulfill client-based contracts, to create sophisticated autonomous systems, but also introduces novel security and privacy (S&P) challenges beyond traditional corporate structures. We conducted an interview study (N=28) with Chinese developers, whose extensive use of global LLM services offer valuable insights into this population. We investigate their understandings, practices and challenges of S&P challenges in their developed AI agent products. We revealed that independent developers frequently think and act from their users' perspective. They focused on user-facing safety risks such as harmful content while exhibiting low awareness of security vulnerabilities. Consequently, developers rely almost exclusively on ad-hoc, manually crafted safeguards and informal communication, with an absence of formal tools or processes for S&P practices. We found these actions are driven by various inhibitors, primarily a lack of formal training on S&P related skills, accessible security tools and actionable guidance from platforms. Our work contributed the first exploration of independent AI agent developers' S&P understanding, outlining opportunities for tailored security tooling.