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When LLMs Manipulate: Measuring Behavioral, Social, and Temporal Effects — arXiv:2610.27573

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.27573
License (as archived): CC0-1.0
Archived: 2026-10-21T07:06:39.237Z
SHA-256 of the archived text: 18ecff308d9f00341f739381e71070e075dd8dee168d648dcd6531bcc021998d
Stable URL of this page: https://metraipolis.com/documento/dc_43ea6bbb6fa5

How to cite

When LLMs Manipulate: Measuring Behavioral, Social, and Temporal Effects — arXiv:2610.27573 — arXiv (en), https://arxiv.org/abs/2610.27573 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_43ea6bbb6fa5 (Metraipolis archive dc_43ea6bbb6fa5, sha256 18ecff308d9f00341f739381e71070e075dd8dee168d648dcd6531bcc021998d)

BibTeX

@misc{dc43ea6bbb6fa5,
  title = {When LLMs Manipulate: Measuring Behavioral, Social, and Temporal Effects — arXiv:2610.27573},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.27573},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_43ea6bbb6fa5; sha256 18ecff308d9f00341f739381e71070e075dd8dee168d648dcd6531bcc021998d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.27573. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Large language models (LLMs) can influence user trust and decision-making through subtle linguistic cues, yet the behavioral effects of manipulative outputs remain poorly quantified. We introduce a behavioral computing framework for measuring \textit{behavioral vulnerability}, defined as users' susceptibility to manipulative AI responses. Building on DarkPatterns-LLM benchmark, we generate controlled manipulative stimuli using Manipulation Gradient Descent (MGD), targeting biases associated with authority, urgency, and social proof. We analyze participant responses using the Multi-Stakeholder Impact Assessment Network (MSIAN) to model cross-user influence and the Temporal Harm Propagation Model (THPM) to characterize the persistence of behavioral effects over time. In a study involving 120 participants, manipulative framing produced significant changes in trust calibration and compliance ($p < 0.01$), with effects also propagating across social contexts. These findings highlight the need to extend LLM safety evaluation beyond model outputs to measurable behavioral consequences and provide a computational framework for quantifying individual, social, and temporal risks arising from manipulative AI interactions.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.27573 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-21T07:05:43.422Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).