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Deauthentication is an important component of any authentication system. The widespread use of computing devices in daily life has underscored the need for zero-effort deauthentication schemes. However, the quest for eliminating user effort may lead to hidden security flaws in the authentication schemes. As a case in point, we investigate a prominent zero-effort deauthentication scheme, called ZEBRA, which provides an interesting and a useful solution to a difficult problem as demonstrated in the original paper. We identify a subtle incorrect assumption in its adversary model that leads to a fundamental design flaw. We exploit this to break the scheme with a class of attacks that are much easier for a human to perform in a realistic adversary model, compared to the naive attacks studied in the ZEBRA paper. For example, one of our main attacks, where the human attacker has to opportunistically mimic only the victims keyboard typing activity at a nearby terminal, is significantly more successful compared to the naive attack that requires mimicking keyboard and mouse activities as well as keyboard-mouse movements. Further, by understanding the design flaws in ZEBRA as cases of tainted input, we show that we can draw on well-understood design principles to improve ZEBRAs security.
This position paper examines potential pitfalls on the way towards achieving human-AI co-creation with generative models in a way that is beneficial to the users interests. In particular, we collected a set of nine potential pitfalls, based on the li
In the age of big data, companies and governments are increasingly using algorithms to inform hiring decisions, employee management, policing, credit scoring, insurance pricing, and many more aspects of our lives. AI systems can help us make evidence
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