Bernoulli honeywords

Decoy passwords, or ``honeywords,'' planted in a credential database can alert a site to its breach if ever submitted in a login attempt. To be effective, some honeywords must appear at least as likely to be user-chosen passwords as the real ones, and honeywords must be very difficult to guess without having breached the database, to prevent false breach alarms. These goals have proved elusive, however, for heuristic honeyword generation algorithms. In this paper we explore an alternative strategy in which the defender treats honeyword selection as a Bernoulli process in which each possible password (except the user-chosen one) is selected as a honeyword independently with some fixed probability. We show how Bernoulli honeywords can be integrated into two existing system designs for leveraging honeywords: one based on a honeychecker that stores the secret index of the user-chosen password in the list of account passwords, and another that does not leverage secret state at all. We show that Bernoulli honeywords enable analytic derivation of false breach-detection probabilities irrespective of what information the attacker gathers about the sites' users; that their true and false breach-detection probabilities demonstrate compelling efficacy; and that Bernoulli honeywords can even enable performance improvements in modern honeyword system designs

The Impact of Exposed Passwords on Honeyword Efficacy

Honeywords are decoy passwords that can be added to a credential database; if a login attempt uses a honeyword, this indicates that the site's credential database has been leaked. In this paper we explore the basic requirements for honeywords to be effective, in a threat model where the attacker knows passwords for the same users at other sites. First, we show that for user-chosen (vs. algorithmically generated, i.e., by a password manager) passwords, existing honeyword-generation algorithms largely fail to achieve reasonable tradeoffs between false positives and false negatives in this threat model. Second, we show that for users leveraging algorithmically generated passwords, state-of-the-art methods for honeyword generation will produce honeywords that are not sufficiently deceptive, yielding many false negatives. Instead, we find that only a honeyword-generation algorithm that uses the same password generator as the user can provide deceptive honeywords in this case. However, when the defender's ability to infer the generator from the (one) account password is less accurate than the attacker's ability to infer the generator from potentially many, this deception can again wane. Taken together, our results provide a cautionary note for the state of honeyword research and pose new challenges to the field