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Anatomy of an AI-powered malicious social botnet

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arxiv 2307.16336 v1 pith:4O674U4K submitted 2023-07-30 cs.CY cs.AIcs.SI

classification cs.CYcs.AIcs.SI
keywords contentaccountsbotnetexhibitfakesocialthemacross
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. BotHash: Efficient and Training-Free Bot Detection Through Approximate Nearest Neighbor

    cs.SI 2025-06 conditional novelty 6.0 of 10

    Approximate nearest-neighbor search over MinHash-encoded behavior sequences detects social bots without training, outperforming several ML baselines on public X/Twitter datasets.

  2. AI Agent Behavioral Science

    q-bio.NC 2025-06 conditional novelty 4.0 of 10

    AI agents should be studied as behavioral entities shaped by context and interaction, not only as trained models.

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