Anatomy of an AI-powered malicious social botnet
July 30, 2023 Β· Declared Dead Β· π Journal of Quantitative Description Digital Media
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Kai-Cheng Yang, Filippo Menczer
arXiv ID
2307.16336
Category
cs.CY: Computers & Society
Cross-listed
cs.AI,
cs.SI
Citations
94
Venue
Journal of Quantitative Description Digital Media
Last Checked
4 months ago
Abstract
Large language models (LLMs) exhibit impressive capabilities in generating realistic text across diverse subjects. Concerns have been raised that they could be utilized to produce fake content with a deceptive intention, although evidence thus far remains anecdotal. This paper presents a case study about a Twitter botnet that appears to employ ChatGPT to generate human-like content. Through heuristics, we identify 1,140 accounts and validate them via manual annotation. These accounts form a dense cluster of fake personas that exhibit similar behaviors, including posting machine-generated content and stolen images, and engage with each other through replies and retweets. ChatGPT-generated content promotes suspicious websites and spreads harmful comments. While the accounts in the AI botnet can be detected through their coordination patterns, current state-of-the-art LLM content classifiers fail to discriminate between them and human accounts in the wild. These findings highlight the threats posed by AI-enabled social bots.
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