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Every year an increasing number of users face stalkerware on their phones [84]. Many of them are victims of intimate partner surveillance (IPS) who are unsure how to identify or remove stalkerware from their phones [49]. An intuitive approach would be to choose anti-stalkerware from the app store. However, a mismatch between user expectations and the technical capabilities can produce an illusion of security and risk compensation behavior (i.e., the Peltzmann effect). We compare users' perceptions of anti-stalkerware with the technical reality. First, we applied thematic analysis to app reviews to analyze user perceptions. Then, we performed a cognitive walkthrough of two prominent anti-stalkerware apps available on the Google Play Store and reverse-engineered them to understand their detection features. Our results suggest that users base their trust on the look and feel of the app, the number and type of alerts, and the apps' affordances. We also found that app capabilities do not correspond to the users' perceptions and expectations, impacting their practical effectiveness. We discuss different stakeholders' options to remedy these challenges and better align user perceptions with the technical reality.

Cryptocurrencies are gaining prominence among individuals and companies alike, resulting in the growing adoption of so-called cryptocurrency wallet applications, as these simplify transactions. These wallets are available in a myriad of different forms and specifications. All of them are susceptible to various ways the attacker can exploit the vulnerabilities and steal money from victims. Cryptocurrency wallets create a unique field as they combine features of password managers, banking applications, and the need to keep their users and their transactions anonymous. We collect the findings from previous literature to provide an overview of the different attack surfaces, possible countermeasures, and further research. Existing literature focused on one of the features mentioned before, while we considered all of them. Our systematic study shows that there is a considerable variety of attack vectors, which we have divided into six subcategories, (i) Memory and Storage, (ii) Operating Systems, (iii) Software Layer, (iv) Network Layer, (v) Blockchain Protocol, and (vi) Others. We have found a large gap between the possible countermeasures and their actual adoption. Therefore, we provide a list of possible directions for future research to tackle this gap.