---
res:
  bibo_abstract:
  - "The recent surge in high-quality open-source Generative AI text models (colloquially:
    LLMs), as well as efficient finetuning techniques, have opened the possibility
    of creating high-quality personalized models that generate text attuned to a specific
    individual’s needs and are capable of credibly imitating their writing style by
    refining an open-source model with that person’s own data. The technology to create
    such models is accessible to private individuals, and training and running such
    models can be done cheaply on consumer-grade hardware. While these advancements
    are a huge gain for usability and privacy, this position paper argues that the
    practical feasibility of impersonating specific individuals also introduces novel
    safety risks. For instance, this technology enables the creation of phishing emails\r\nor
    fraudulent social media accounts, based on small amounts of publicly available
    text, or by the individuals themselves to escape AI text detection. We further
    argue that these risks are complementary to—and distinct from—the much-discussed
    risks of other impersonation attacks such as image, voice, or video deepfakes,
    and are not adequately addressed by the larger research community, or the current
    generation of open- and closed-source models.@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Eugenia B
      foaf_name: Iofinova, Eugenia B
      foaf_surname: Iofinova
      foaf_workInfoHomepage: http://www.librecat.org/personId=f9a17499-f6e0-11ea-865d-fdf9a3f77117
    orcid: 0000-0002-7778-3221
  - foaf_Person:
      foaf_givenName: Andrej
      foaf_name: Jovanovic, Andrej
      foaf_surname: Jovanovic
  - foaf_Person:
      foaf_givenName: Dan-Adrian
      foaf_name: Alistarh, Dan-Adrian
      foaf_surname: Alistarh
      foaf_workInfoHomepage: http://www.librecat.org/personId=4A899BFC-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0003-3650-940X
  bibo_doi: 10.48550/arXiv.2502.06560
  dct_date: 2025^xs_gYear
  dct_language: eng
  dct_title: 'Position: It''s time to act on the risk of efficient personalized text
    generation@'
...