  Contrastive learning (CL) has recently gained prominence in the domain of
recommender systems due to its great ability to enhance recommendation accuracy
and improve model robustness. Despite its advantages, this paper identifies a
vulnerability of CL-based recommender systems that they are more susceptible to
poisoning attacks aiming to promote individual items. Our analysis indicates
that this vulnerability is attributed to the uniform spread of representations
caused by the InfoNCE loss. Furthermore, theoretical and empirical evidence
shows that optimizing this loss favors smooth spectral values of
representations. This finding suggests that attackers could facilitate this
optimization process of CL by encouraging a more uniform distribution of
spectral values, thereby enhancing the degree of representation dispersion.
With these insights, we attempt to reveal a potential poisoning attack against
CL-based recommender systems, which encompasses a dual-objective framework: one
that induces a smoother spectral value distribution to amplify the InfoNCE
loss's inherent dispersion effect, named dispersion promotion; and the other
that directly elevates the visibility of target items, named rank promotion. We
validate the threats of our attack model through extensive experimentation on
four datasets. By shedding light on these vulnerabilities, our goal is to
advance the development of more robust CL-based recommender systems. The code
is available at \url{https://github.com/CoderWZW/ARLib}.
