Extant research has focused on the detection of fake reviews on online review platforms, motivated by the well-documented impact of customer reviews on the users' purchase decisions. The problem is typically approached from the perspective of protecting the credibility of review platforms, as well as the reputation and revenue of the reviewed firms. However, there is little examination of the vulnerability of individual businesses to fake review attacks. This study focuses on formalizing the visibility of a business to the customer base and on evaluating its vulnerability to fake review attacks. We operationalize visibility as a function of the features that a business can cover and its position in the platform's review-based ranking. Using data from over 2.3 million reviews of 4,709 hotels from 17 cities, we study how visibility can be impacted by different attack strategies. We find that even limited injections of fake reviews can have a significant effect and explore the factors that contribute to this vulnerable state. Specifically, we find that, in certain markets, 50 fake reviews are sufficient for an attacker to surpass any of its competitors in terms of visibility. We also compare the strategy of self-injecting positive reviews with that of injecting competitors with negative reviews and find that each approach can be as much as 40% more effective than the other across different settings. We empirically explore response strategies for an attacked hotel, ranging from the enhancement of its own features to detecting and disputing fake reviews. In general, our measure of visibility and our modeling approach regarding attack and response strategies shed light on how businesses that are targeted by fake reviews can detect and tackle such attacks.