Adversarial Machine Learning for Robust Password Strength Estimation

Ashim Dahal; Hanzla Hamid; Nick Rahimi; Oluseyi Olukola; Pappu Jha

REVIEW 4 major objections 4 minor 7 references

Adversarial Machine Learning for Robust Password Strength Estimation

T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Adversarial training improves password strength classification accuracy by up to 20% compared to traditional machine learning models.

desk verdict The paper's adversarial-training claim is asserted, not measured: no adversarial examples are generated and no baseline is compared, so the 20% improvement is unsupported, though the underlying classification benchmark is standard and cleanly described. read the letter →

arxiv 2506.00373 v1 pith:NLIXCO4N submitted 2025-05-31 cs.CR

Pappu Jha , Hanzla Hamid , Oluseyi Olukola , Ashim Dahal , Nick Rahimi This is my paper

classification cs.CR

keywords adversarialattackpasswordstrengthclassificationmachinelearningcharactersubstitutiongradientboostingcross-validationsecurity

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that password strength classifiers become markedly more accurate on tricky, deceptive inputs when they are trained on datasets that contain adversarial passwords. Using more than 670,000 password samples, the authors trained five classifiers—Random Forest, Logistic Regression, Naive Bayes, Decision Tree, and gradient-boosted trees—and report accuracies between 94% and 99%, with gains of up to 20% over earlier models trained only on ordinary passwords. The practical point is that simple substitutions like 'p@ssword' can fool rule-based strength meters, and training on such examples is presented as a way to stop that deception. The paper itself notes in its conclusion that the adversarial examples were naturally present in the downloaded data rather than deliberately crafted for the experiment.

What carries the argument

The central object is the training dataset itself: more than 670,000 password-strength pairs that contain incidental adversarial instances, chiefly character substitutions and deceptive complexity. The mechanism is supervised classification with class weighting and synthetic minority oversampling to correct class imbalance, 5-fold cross-validation to test generalization, and learning curves to monitor overfitting. The models—Random Forest, Logistic Regression, Naive Bayes, Decision Tree, and gradient-boosted trees—are the classifiers through which the adversarial training effect is measured, with the gradient-boosted model hyperparameter-tuned by grid search. The dataset's deceptive examples are what carry the argument: they are the reason the trained models are claimed to recognize substituted passwords as deceptive rather than strong.

What would settle it

A direct test would hold out a set of deliberately substituted passwords and compare models trained with and without such substitutions; if the clean-trained models match the adversarial-trained models on that held-out set, the claimed 20% advantage is not attributable to adversarial training.

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Extended reading notes

Core claim

The paper's central claim is that adversarial training—exposing classifiers to deceptive password examples during training—yields password strength models that are substantially more robust to adversarial inputs. On a combined dataset of 670,000+ passwords, the best models (Random Forest, Logistic Regression, Decision Tree, and gradient-boosted trees) reach 99% accuracy and 0.99 precision, recall, and F1-score, while Naive Bayes reaches 94%. The authors report that these models outperform traditional models by up to 20% and read this as evidence that integrating adversarial examples into training data is a practical defense against adversarial password attacks. They support the result with class balancing, 5-fold cross-validation, and learning curves to argue the high accuracy is not overfitting.

Load-bearing premise

The load-bearing premise is that the incidental character substitutions and deceptive complexity found in the collected passwords are a valid stand-in for deliberate adversarial attacks, so that training on them qualifies as adversarial training.

Editorial extensions

If this is right

Password strength checkers trained this way should classify substituted passwords such as 'p@ssword' in the same strength category as their unsubstituted counterparts.
The reported 20% improvement implies that conventional clean-data password classifiers are vulnerable to character-substitution attacks.
The cross-validation and learning-curve checks support the claim that the accuracy gains are not just memorization of the training set.
Incorporating adversarial examples into training data is a low-cost route to hardening user-authentication systems without changing password rules.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Editorial inference: because the adversarial samples were incidental rather than crafted, the 20% figure should be read as a demonstration that deceptive data can shift classifier behavior, not as proof that deliberate adversarial training is optimal.
Editorial inference: the same substitution-based attack surface could be measured directly by training one model on clean passwords and another on the same passwords with systematic substitutions, isolating the effect of the adversarial training signal.
Editorial inference: the approach could be extended to password-guessing models, where robustness to adversarial inputs has a more direct security consequence than in strength classification alone.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The headline claim that adversarial training improves password strength classification by up to 20% is not supported by the experiments. No adversarial examples are generated, no baseline model trained on ordinary passwords is reported, and the 20% figure appears only in the abstract and conclusion. The datasets are described as containing naturally present deceptive passwords, which the authors themselves concede in Section 5 may not fully capture the essence of adversarial inputs. That concession is honest, but it guts the central argument: training on a mixed Kaggle dataset with some character substitutions is just standard classification on that dataset, not adversarial training in any meaningful sense. What the paper does well is the ordinary supervised classification part. The literature review in Section 2 is organized and critical, and the experimental setup with five classifiers, an 80/20 split, 5-fold cross-validation, class weighting, SMOTE, and learning curves is conventional and would be fine for a benchmark study. The results in Table 2 are plausible for this kind of task, with most models hitting 99% accuracy. The soft spots are proportionate to the claim. The biggest one is the missing comparison: without a model trained on clean, non-adversarial data, there is no way to attribute any accuracy gain to adversarial training. The second is a likely leaky feature: estimated crack time from the second dataset is almost certainly derived from the same strength ratings used as labels, so the models may be reading the answer off the feature. Third, the support counts in Table 2 do not match the stated 80/20 split of the combined dataset, since Random Forest shows about 298,000 test samples when about 134,000 is expected, which suggests an inconsistency in how the split or the table was generated. Finally, the introduction says the models were tested with custom adversarial passwords, but no such test results appear anywhere. If the adversarial framing were stripped away, the paper would be a mundane but serviceable comparison of five classifiers on a large password dataset, not a novel contribution but not actively misleading. As written, the central claim is overloaded, and a reader who trusts the abstract would be misled. I would not send this to peer review in its current form; it needs either real adversarial examples with the missing baseline or an honest reframing as a regular classification study. The authors should also fix the support count and either remove the leaky feature or show results without it. For a reading group, it is a decent case study in how easily an unsupported claim can slip into an otherwise standard ML paper.

Referee Report

4 major / 4 minor

Summary. The paper proposes using adversarial machine learning to improve password strength classification. The authors combine two Kaggle datasets (~670,000 passwords), extract features such as password length and estimated crack time, and train five classifiers (Random Forest, Logistic Regression, Naive Bayes, Decision Tree, XGBoost). The abstract and conclusion claim that adversarial training improves accuracy by up to 20% over traditional machine learning models. However, the reported experiments consist only of standard supervised classification on a single combined dataset, with no baseline trained on non-adversarial data, no generated adversarial examples, and no direct measurement of the claimed improvement.

Significance. If the central claim were properly supported, the work would address a real gap in password strength estimation: robustness to deceptive inputs such as 'p@ssword'. The paper provides a useful literature review and uses a large dataset, but it does not deliver a falsifiable test of adversarial robustness. There is no code, no machine-checked derivation, and no parameter-free predictive claim; the headline result is asserted rather than demonstrated. The main value of the manuscript is as a survey and a preliminary empirical report, not as a validated contribution to adversarial machine learning.

major comments (4)

[Abstract, Section 5, Table 2] The claim that adversarial training improves accuracy by up to 20% over traditional models is not supported by any reported comparison. Table 2 lists precision, recall, F1, support, and accuracy for five models on the combined dataset, but there is no baseline model trained on ordinary (non-adversarial) passwords, and no figure or table reports the 20% figure. The improvement is stated in the Abstract and Conclusion, yet it is never measured. To support this claim, the authors must train and evaluate a non-adversarial baseline and report the accuracy difference.
[Sections 3.1 and 5] The experiments do not actually perform adversarial training. Section 3.1 states that the datasets 'contained a common presence of password properties related to adversarial attacks' that are 'naturally present,' and Section 5 concedes that these samples 'may not fully capture the essence of the adversarial inputs.' No adversarial examples are generated, and no attack is simulated. As reported, the experiment is standard supervised classification on a password dataset, so the central claim about adversarial robustness is untestable in the current setup.
[Section 3.2] The feature set includes 'estimated crack time' and 'class strength,' but the paper does not explain how 'estimated crack time' is computed. If this feature is derived from the same strength labels found in the Kaggle datasets (Dataset 2 has a numeric strength column that is later re-labeled), the model's near-perfect accuracy could result from data leakage rather than from learning a robust mapping. The authors must clarify the provenance of this feature; if it is label-derived, it should be removed and the experiments rerun.
[Table 2] The Support values in Table 2 are inconsistent across models: Random Forest reports 298,295, Naive Bayes 201,031, and the other three models 134,021. Since all models are trained and tested on the same 80/20 split of a single dataset, the test set should be identical, yielding the same support. This discrepancy suggests either a reporting error or that the models were evaluated on different subsets, which undermines the comparability of the reported accuracies.

minor comments (4)

[Section 1, references] The citation [3] is used for Password Meter, Microsoft Password Checker, and Google Password Meter, but [3] refers to the paper by Sarkar and Nandan, not to these online tools; the reference appears incorrect.
[Throughout] There are several typographical and formatting issues: 'T able 1' and 'T able 2' should be 'Table 1' and 'Table 2'; 'Random Forrest' should be 'Random Forest'; 'week' should be 'weak' in Section 3.1; and the introduction refers to 'Section II' and 'Section III' while the paper uses Arabic numbering.
[Sections 3.1 and 3.3] The text states that heatmaps and learning curves were generated, but no heatmap or learning-curve figures are included; the only figures are the cross-validation plot (Fig. 2), confusion matrix (Fig. 3), and performance curves (Figs. 4-6).
[Figure 2] The caption of Figure 2 reads '5-Fold Cross-Validation in Naive Bayes,' but the text says 5-fold cross-validation was performed on all models; results for the other models are not shown.

Circularity Check

3 steps flagged · score 6.0 of 10

The 20% adversarial-training improvement is asserted without any non-adversarial baseline, and the reported accuracies are partly artifacts of label-derived features, so the robustness claim reduces to within-distribution classification on self-declared adversarial data.

self definitional [Section 3.2 (Feature Extraction) and Section 3.1 (Data collection and preprocessing)]
"we chose the most relevant features—including password length, the number of unique, null, and duplicate values, estimated crack time, and class strength—for model training. … strengths from 1 to 4 were labeled as 0, 5 to 8 as 1, and 9 and above as 2."

The models' prediction target is the strength class (0/1/2), yet Section 3.2 lists 'class strength' itself as a training feature, so a classifier can reach its reported accuracy by reading the label from its inputs. Furthermore, 'estimated crack time' derives from the same 0-50 strength rating that Section 3.1 bins into the target classes (1-4→0, 5-8→1, 9+→2), making the feature a near-deterministic function of the target. The 99% accuracies in Table 2 are therefore partly artifacts of feature-label overlap, not measurements of robustness to adversarial passwords.
fitted input called prediction [Abstract and Section 5 (Conclusion), versus Section 3.1 and Table 2]
"Results demonstrate that adversarial training improves password strength classification accuracy by up to 20% compared to traditional machine learning models. … the samples of adversarial passwords were naturally present in the datasets, which may not fully capture the essence of the adversarial inputs while training the models."

The claimed prediction—a 20% advantage over 'traditional models'—is never derived in the paper: Table 2 reports only the five classifiers on the single mixed dataset, with no non-adversarial baseline, no crafted attack set, and no comparison to the Section 2 prior work. The adversarial status of the inputs is self-declared from the training data (Section 3.1 calls naturally occurring character substitutions and 'deceptive complexity' 'effective adversarial inputs'), so the reported accuracy is within-distribution classification relabeled as adversarial robustness. With no control group, the improvement is asserted rather than measured, and Section 5 concedes the data 'may not fully capture the essence of the adversarial inputs.'

1 more flagged steps

renaming known result [Section 1 (Introduction) and Section 3.1 (Data collection and preprocessing)]
"adversarial passwords are deliberately designed to trick algorithms, causing a discrepancy between their actual strength and the strength assessed by a model. For example, if 'password' is determined as weak, 'p@ssword' will be classified as strong … In this dataset, there was a common presence of password properties related to adversarial attacks, namely character substitutions and deceptive complexity."

The Introduction defines adversarial passwords as deliberately crafted inputs ('p@ssword' deceiving a strength meter), but the datasets are ordinary Kaggle corpora whose substituted and complex strings, in the paper's own words, 'were naturally present in the datasets' (Section 5). Labeling standard supervised classification on these corpora 'adversarial training' renames ordinary password-strength classification to match the headline claim: the operational meaning of 'adversarial' is supplied by whatever the training data happens to contain, so the resulting robustness result is a relabeling rather than a test of the phenomenon defined in Section 1.

full rationale

There are no self-citations, so patterns 3-5 (self-citation load-bearing, imported uniqueness, ansatz via citation) do not apply. The circularity is internal to the experimental construction. (1) Section 3.2 lists 'class strength' among the features used for training even though the strength class is the prediction target, and 'estimated crack time' comes from the same 0-50 strength rating that Section 3.1 bins into the target classes, so the Table 2 accuracies partly measure the label's own value. (2) The central claim—'adversarial training improves password strength classification accuracy by up to 20%'—is never derived: Table 2 has no traditional (non-adversarial) baseline and no comparison to the Section 2 papers, so the improvement is an assertion, not a measurement; the Introduction's promised custom adversarial test ('predicted correctly with high accuracy') never appears in Section 4, leaving only within-distribution evidence. (3) The adversarial character of the data is self-declared in Section 3.1 and then conceded in Section 5 as possibly not capturing 'the essence of the adversarial inputs,' which renames ordinary Kaggle classification as adversarial training. On balance this is partial circularity: the accuracy figures are confounded by construction and the robustness prediction reduces to in-distribution fit, while the 20% figure itself is unsupported rather than derived in a closed loop—a support gap that amplifies, but is distinct from, the circularity.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The central claim depends on the reliability of third-party labels and on the adequacy of incidental adversarial examples. No new formal entities are introduced, but the free parameters include hand-chosen class thresholds, unreported hyperparameters, and undocumented class-balancing settings.

free parameters (3)

Dataset 2 strength bin thresholds = 1-4 weak, 5-8 medium, 9+ strong
Hand-chosen mapping to convert the second dataset's numeric strength scores into three classes; changing these bins changes the labels and therefore all reported accuracies.
XGBoost hyperparameters = not reported
Tuned with GridSearchCV, but final values for learning rate, tree depth, and number of estimators are not reported, so the claimed performance depends on unstated tuning choices.
Class weights and SMOTE settings = not reported
Class imbalance handling is described qualitatively; the exact weights or SMOTE parameters are not given, yet they affect precision, recall, and accuracy.

assumptions (3)

domain assumption Kaggle strength labels are correct ground truth.
The models are trained and evaluated directly on the third-party labels with no external validation of what 'weak', 'medium', or 'strong' should mean.
ad hoc to paper Incidental adversarial properties in the datasets represent realistic adversarial attacks.
Section 3.1 relies on 'character substitutions and deceptive complexity' as adversarial inputs, while Section 5 admits these were naturally present and may not capture the essence of adversarial inputs.
ad hoc to paper Estimated crack time is a valid predictive feature distinct from the strength class.
Section 3.2 lists estimated crack time as a feature; if that quantity is computed from the same strength definition as the target label, it leaks the answer into the features.

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Cite this review

Pith. "Pith review of Adversarial Machine Learning for Robust Password Strength Estimation." pith.science (2026). https://pith.science/paper/NLIXCO4N

@misc{pith2026250600373,
  author       = {Pith},
  title        = {Pith review of: Adversarial Machine Learning for Robust Password Strength Estimation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NLIXCO4N}},
  note         = {Machine review of arXiv:2506.00373}
}

read the original abstract

Passwords remain one of the most common methods for securing sensitive data in the digital age. However, weak password choices continue to pose significant risks to data security and privacy. This study aims to solve the problem by focusing on developing robust password strength estimation models using adversarial machine learning, a technique that trains models on intentionally crafted deceptive passwords to expose and address vulnerabilities posed by such passwords. We apply five classification algorithms and use a dataset with more than 670,000 samples of adversarial passwords to train the models. Results demonstrate that adversarial training improves password strength classification accuracy by up to 20% compared to traditional machine learning models. It highlights the importance of integrating adversarial machine learning into security systems to enhance their robustness against modern adaptive threats. Keywords: adversarial attack, password strength, classification, machine learning

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Reference graph

Works this paper leans on

7 extracted references · 5 canonical work pages

[1]

International Journal of Computer Applications, 7(14), 1–5 (2010)

Suganya, G., Karpgavalli, S., Christina, V.: Proactive Password Strength Ana- lyzer Using Filters and Machine Learning Techniques. International Journal of Computer Applications, 7(14), 1–5 (2010). doi:10.5120/1333-1788

work page doi:10.5120/1333-1788 2010
[2]

In: 2024 Joint Int’l Conf

Asaduzzaman, A., D’Souza, D., Uddin, M.R., Woldeyes, Y.: Increase Secu- rity by Analyzing Password Strength using Machine Learning. In: 2024 Joint Int’l Conf. on Digital Arts, Media and Technology with ECTI Northern Section Conf. (ECTI DAMT & NCON) , pp. 32–37 (2024). doi:10.1109/ECTIDAMTNCON60518.2024.10479995

arXiv 2024
[3]

In:2022 Second International Con- ference on Computer Science, Engineering and Applications (ICCSEA), pp

Sarkar, S., Nandan, M.: Password Strength Analysis and its Classification by Applying Machine Learning Based Techniques. In:2022 Second International Con- ference on Computer Science, Engineering and Applications (ICCSEA), pp. 1–5 (2022). doi:10.1109/ICCSEA54677.2022.9936117

work page arXiv 2022
[4]

In: 2024 Global Conference on Wireless and Optical Technologies (GCWOT), pp

Rehman, H., Ahmed, A., Hussain, A., Umar, M., Siraj, M., Ahmed, W., Muham- nad, M., Rehman, E.: Password Strength Classification Using Machine Learning Methods. In: 2024 Global Conference on Wireless and Optical Technologies (GCWOT), pp. 1–7 (2024). doi:10.1109/GCWOT63882.2024.10805622

work page arXiv 2024
[5]

In: 2009 International Confer- ence on Advances in Computing, Control, and Telecommunication Technologies, pp

Vijaya, M.S., Jamuna, K.S., Karpagavalli, S.: Password Strength Prediction Using Supervised Machine Learning Techniques. In: 2009 International Confer- ence on Advances in Computing, Control, and Telecommunication Technologies, pp. 401–405 (2009). doi:10.1109/ACT.2009.105

work page doi:10.1109/act.2009.105 2009
[6]

In: 2020 IEEE 19th International 12 Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), pp

Zhang, T., Cheng, Z., Qin, Y., Li, Q., Shi, L.: Deep Learning for Password Guessing and Password Strength Evaluation, A Survey. In: 2020 IEEE 19th International 12 Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), pp. 1162–1166 (2020). doi:10.1109/TrustCom50675.2020.00155

arXiv 2020
[7]

In: 2017 International Conference on Trends in Electronics and Informatics (ICEI) , pp

Pagar, V.R., Pise, R.G.: Strengthening Password Security through Hon- eyword and Honeyencryption Technique. In: 2017 International Conference on Trends in Electronics and Informatics (ICEI) , pp. 827–831 (2017). doi:10.1109/ICOEI.2017.8300819 13

work page arXiv 2017

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.

[1] [1]

International Journal of Computer Applications, 7(14), 1–5 (2010)

Suganya, G., Karpgavalli, S., Christina, V.: Proactive Password Strength Ana- lyzer Using Filters and Machine Learning Techniques. International Journal of Computer Applications, 7(14), 1–5 (2010). doi:10.5120/1333-1788

work page doi:10.5120/1333-1788 2010

[2] [2]

In: 2024 Joint Int’l Conf

Asaduzzaman, A., D’Souza, D., Uddin, M.R., Woldeyes, Y.: Increase Secu- rity by Analyzing Password Strength using Machine Learning. In: 2024 Joint Int’l Conf. on Digital Arts, Media and Technology with ECTI Northern Section Conf. (ECTI DAMT & NCON) , pp. 32–37 (2024). doi:10.1109/ECTIDAMTNCON60518.2024.10479995

arXiv 2024

[3] [3]

In:2022 Second International Con- ference on Computer Science, Engineering and Applications (ICCSEA), pp

Sarkar, S., Nandan, M.: Password Strength Analysis and its Classification by Applying Machine Learning Based Techniques. In:2022 Second International Con- ference on Computer Science, Engineering and Applications (ICCSEA), pp. 1–5 (2022). doi:10.1109/ICCSEA54677.2022.9936117

work page arXiv 2022

[4] [4]

In: 2024 Global Conference on Wireless and Optical Technologies (GCWOT), pp

Rehman, H., Ahmed, A., Hussain, A., Umar, M., Siraj, M., Ahmed, W., Muham- nad, M., Rehman, E.: Password Strength Classification Using Machine Learning Methods. In: 2024 Global Conference on Wireless and Optical Technologies (GCWOT), pp. 1–7 (2024). doi:10.1109/GCWOT63882.2024.10805622

work page arXiv 2024

[5] [5]

In: 2009 International Confer- ence on Advances in Computing, Control, and Telecommunication Technologies, pp

Vijaya, M.S., Jamuna, K.S., Karpagavalli, S.: Password Strength Prediction Using Supervised Machine Learning Techniques. In: 2009 International Confer- ence on Advances in Computing, Control, and Telecommunication Technologies, pp. 401–405 (2009). doi:10.1109/ACT.2009.105

work page doi:10.1109/act.2009.105 2009

[6] [6]

In: 2020 IEEE 19th International 12 Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), pp

Zhang, T., Cheng, Z., Qin, Y., Li, Q., Shi, L.: Deep Learning for Password Guessing and Password Strength Evaluation, A Survey. In: 2020 IEEE 19th International 12 Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), pp. 1162–1166 (2020). doi:10.1109/TrustCom50675.2020.00155

arXiv 2020

[7] [7]

In: 2017 International Conference on Trends in Electronics and Informatics (ICEI) , pp

Pagar, V.R., Pise, R.G.: Strengthening Password Security through Hon- eyword and Honeyencryption Technique. In: 2017 International Conference on Trends in Electronics and Informatics (ICEI) , pp. 827–831 (2017). doi:10.1109/ICOEI.2017.8300819 13

work page arXiv 2017