# When AI Defeats Password Deception! A Deep Learning Framework to Distinguish Passwords and Honeywords

arXiv: https://arxiv.org/abs/2407.16964 · HTML: https://mlsanity.com/paper/2407.16964/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 50 references that are in the map.
- First posted on arXiv 24 Jul 2024.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://mlsanity.com/paper/1712.03141/) (2017)
- [Deep Fingerprinting: Undermining Website Fingerprinting Defenses with Deep Learning](https://mlsanity.com/paper/1801.02265/) (2018)
- [PassGAN: A Deep Learning Approach for Password Guessing](https://mlsanity.com/paper/1709.00440/) (2017)
- [Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics](https://mlsanity.com/paper/1907.07296/) (2019)
- [Fingerprinting the Fingerprinters: Learning to Detect Browser Fingerprinting Behaviors](https://mlsanity.com/paper/2008.04480/) (2020)
- [Generating High Fidelity Synthetic Data via Coreset selection and Entropic Regularization](https://mlsanity.com/paper/2302.00138/) (2023)
- [Targeted Honeyword Generation with Language Models](https://mlsanity.com/paper/2208.06946/) (2022)
- [Bernoulli honeywords](https://mlsanity.com/paper/2212.12759/) (2022)
- [A New Storage Optimized Honeyword Generation Approach for Enhancing Security and Usability](https://mlsanity.com/paper/1509.06094/) (2015)

## Built on by (most cited first, 2 of 2)

- [MAYA: Addressing Inconsistencies in Generative Password Guessing through a Unified Benchmark](https://mlsanity.com/paper/2504.16651/) (2025)
- [Secu-Table: a Comprehensive security table dataset for evaluating semantic table interpretation systems](https://mlsanity.com/paper/2511.06301/) (2025)

## Abstract (arXiv)

"Honeywords" have emerged as a promising defense mechanism for detecting data breaches and foiling offline dictionary attacks (ODA) by deceiving attackers with false passwords. In this paper, we propose PassFilter, a novel deep learning (DL) based attack framework, fundamental in its ability to identify passwords from a set of sweetwords associated with a user account, effectively challenging a variety of honeywords generation techniques (HGTs). The DL model in PassFilter is trained with a set of previously collected or adversarially generated passwords and honeywords, and carefully orchestrated to predict whether a sweetword is the password or a honeyword. Our model can compromise the security of state-of-the-art, heuristics-based, and representation learning-based HGTs proposed by Dionysiou et al. Specifically, our analysis with nine publicly available password datasets shows that PassFilter significantly outperforms the baseline random guessing success rate of 5%, achieving 6.10% to 52.78% on the 1st guessing attempt, considering 20 sweetwords per account. This success rate rapidly increases with additional login attempts before account lock-outs, often allowed on many real-world online services to maintain reasonable usability. For example, it ranges from 41.78% to 96.80% for five attempts, and from 72.87% to 99.00% for ten attempts, compared to 25% and 50% random guessing, respectively. We also examined PassFilter against general-purpose language models used for honeyword generation, like those proposed by Yu et al. These honeywords also proved vulnerable to our attack, with success rates of 14.19% for 1st guessing attempt, increasing to 30.23%, 41.70%, and 63.10% after 3rd, 5th, and 10th guessing attempts, respectively. Our findings demonstrate the effectiveness of DL model deployed in PassFilter in breaching state-of-the-art HGTs and compromising password security based on ODA.