---
OA_place: repository
OA_type: green
_id: '21858'
abstract:
- lang: eng
  text: "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."
acknowledgement: "This research was supported by the Scientific Service Units (SSU)
  of IST Austria through resources\r\nprovided by Scientific Computing (SciComp).
  EI was supported in part by the FWF DK VGSCO,\r\ngrant agreement number W1260-N35.
  AJ was supported in part by ERC Proof-of-Concept Grant\r\nFastML, grant agreement
  101158077."
article_processing_charge: No
arxiv: 1
author:
- first_name: Eugenia B
  full_name: Iofinova, Eugenia B
  id: f9a17499-f6e0-11ea-865d-fdf9a3f77117
  last_name: Iofinova
  orcid: 0000-0002-7778-3221
- first_name: Andrej
  full_name: Jovanovic, Andrej
  last_name: Jovanovic
- first_name: Dan-Adrian
  full_name: Alistarh, Dan-Adrian
  id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
  last_name: Alistarh
  orcid: 0000-0003-3650-940X
citation:
  ama: 'Iofinova EB, Jovanovic A, Alistarh D-A. Position: It’s time to act on the
    risk of efficient personalized text generation. <i>arXiv</i>. doi:<a href="https://doi.org/10.48550/arXiv.2502.06560">10.48550/arXiv.2502.06560</a>'
  apa: 'Iofinova, E. B., Jovanovic, A., &#38; Alistarh, D.-A. (n.d.). Position: It’s
    time to act on the risk of efficient personalized text generation. <i>arXiv</i>.
    <a href="https://doi.org/10.48550/arXiv.2502.06560">https://doi.org/10.48550/arXiv.2502.06560</a>'
  chicago: 'Iofinova, Eugenia B, Andrej Jovanovic, and Dan-Adrian Alistarh. “Position:
    It’s Time to Act on the Risk of Efficient Personalized Text Generation.” <i>ArXiv</i>,
    n.d. <a href="https://doi.org/10.48550/arXiv.2502.06560">https://doi.org/10.48550/arXiv.2502.06560</a>.'
  ieee: 'E. B. Iofinova, A. Jovanovic, and D.-A. Alistarh, “Position: It’s time to
    act on the risk of efficient personalized text generation,” <i>arXiv</i>. .'
  ista: 'Iofinova EB, Jovanovic A, Alistarh D-A. Position: It’s time to act on the
    risk of efficient personalized text generation. arXiv, <a href="https://doi.org/10.48550/arXiv.2502.06560">10.48550/arXiv.2502.06560</a>.'
  mla: 'Iofinova, Eugenia B., et al. “Position: It’s Time to Act on the Risk of Efficient
    Personalized Text Generation.” <i>ArXiv</i>, doi:<a href="https://doi.org/10.48550/arXiv.2502.06560">10.48550/arXiv.2502.06560</a>.'
  short: E.B. Iofinova, A. Jovanovic, D.-A. Alistarh, ArXiv (n.d.).
corr_author: '1'
date_created: 2026-05-11T08:55:23Z
date_published: 2025-06-02T00:00:00Z
date_updated: 2026-05-19T11:20:27Z
day: '02'
department:
- _id: GradSch
- _id: DaAl
doi: 10.48550/arXiv.2502.06560
external_id:
  arxiv:
  - '2502.06560'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.48550/arXiv.2502.06560
month: '06'
oa: 1
oa_version: Preprint
project:
- _id: 8e35c14b-16d5-11f0-9cad-a3fc35339161
  grant_number: '101158077'
  name: 'FastML: Efficient and Cost-Effective Distributed Machine Learning'
- _id: 9B9290DE-BA93-11EA-9121-9846C619BF3A
  grant_number: W1260-N35
  name: Vienna Graduate School on Computational Optimization
publication: arXiv
publication_status: draft
related_material:
  record:
  - id: '21854'
    relation: dissertation_contains
    status: public
status: public
title: 'Position: It''s time to act on the risk of efficient personalized text generation'
type: preprint
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2025'
...