REVIEW 2 cited by
Anatomy of an AI-powered malicious social botnet
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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.
Forward citations
Cited by 2 Pith papers
-
BotHash: Efficient and Training-Free Bot Detection Through Approximate Nearest Neighbor
Approximate nearest-neighbor search over MinHash-encoded behavior sequences detects social bots without training, outperforming several ML baselines on public X/Twitter datasets.
-
AI Agent Behavioral Science
AI agents should be studied as behavioral entities shaped by context and interaction, not only as trained models.
Discussion (0). Continue with ORCID to comment.