Using Amnesia to Detect Credential Database Breaches


Ke Coby Wang, University of North Carolina at Chapel Hill; Michael K. Reiter, Duke University


Known approaches for using decoy passwords (honeywords) to detect credential database breaches suffer from the need for a trusted component to recognize decoys when entered in login attempts, and from an attacker's ability to test stolen passwords at other sites to identify user-chosen passwords based on their reuse at those sites. Amnesia is a framework that resolves these difficulties. Amnesia requires no secret state to detect the entry of honeywords and additionally allows a site to monitor for the entry of its decoy passwords elsewhere. We quantify the benefits of Amnesia using probabilistic model checking and the practicality of this framework through measurements of a working implementation.

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@inproceedings {272240,
author = {Ke Coby Wang and Michael K. Reiter},
title = {Using Amnesia to Detect Credential Database Breaches},
booktitle = {30th {USENIX} Security Symposium ({USENIX} Security 21)},
year = {2021},
isbn = {978-1-939133-24-3},
pages = {839--855},
url = {},
publisher = {{USENIX} Association},
month = aug,

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