机器人的攻击:从受损的在线MSM调查中吸取教训
Abson Madola1,2, Michael DeWitt1,2, Jennifer Wenner1
1Section on Infectious Diseases, https://ror.org/04v8djg66Wake Forest University School of Medicine, Winston-Salem, NC, USA.
Epidemiology and infection
|August 15, 2025
概括
匿名在线调查对于研究性网络至关重要,受到复杂的机器人的威胁. 实施强大的数据验证对于保持对不断变化的AI威胁的研究完整性至关重要.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 行为科学 行为科学
背景情况:
- 有经济激励的匿名在线调查对于了解性网络,行为和风险因素至关重要.
- 这些调查容易受到机器人利用,从而损害数据完整性.
- 先进的人工智能的兴起,包括大型语言模型,加剧了复杂的机器人攻击的威胁.
研究的目的:
- 评估与男性发生性关系的男性 (MSM) 中的地理定位应用程序使用情况.
- 描述应用程序使用对性行为和感染风险的影响.
- 突出在线调查对机器人利用的脆弱性和需要加强安全性.
主要方法:
- 在北卡罗来纳州,通过QR码进行了面对面的,有限的受众调查.
- 描述性统计数据被用来分析重复响应,自由文本长度和人口统计的一致性.
- 这项研究在社交媒体上意外传播,导致回应激增.
主要成果:
- 该调查在2022年8月至2023年3月期间收到了4,709份回复.
- 在调查病毒化后,响应量急剧上升,一天内有2000多个响应.
- 虽然自由文本答案是复杂的,但许多多选项答案是不一致的,表明潜在的机器人活动.
结论:
- 在线调查数据质量受到复杂的机器人攻击的威胁,特别是使用先进的人工智能.
- 防御技术,如响应时间验证,逻辑检查和知识产权选,对于保护研究完整性至关重要.
- 积极实施安全措施对于在线研究中可靠的数据收集至关重要.
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