  Text-aware recommender systems incorporate rich textual features, such as
titles and descriptions, to generate item recommendations for users. The use of
textual features helps mitigate cold-start problems, and thus, such recommender
systems have attracted increased attention. However, we argue that the
dependency on item descriptions makes the recommender system vulnerable to
manipulation by adversarial sellers on e-commerce platforms. In this paper, we
explore the possibility of such manipulation by proposing a new text rewriting
framework to attack text-aware recommender systems. We show that the rewriting
attack can be exploited by sellers to unfairly uprank their products, even
though the adversarially rewritten descriptions are perceived as realistic by
human evaluators. Methodologically, we investigate two different variations to
carry out text rewriting attacks: (1) two-phase fine-tuning for greater attack
performance, and (2) in-context learning for higher text rewriting quality.
Experiments spanning 3 different datasets and 4 existing approaches demonstrate
that recommender systems exhibit vulnerability against the proposed text
rewriting attack. Our work adds to the existing literature around the
robustness of recommender systems, while highlighting a new dimension of
vulnerability in the age of large-scale automated text generation.
