The Tables Have Turned: GPT-3 Distinguishing Passwords from Honeywords
Bibliographic record
Abstract
In the field of information security, there has been a noteworthy trend toward leveraging machine learning models to develop and exploit security solutions. The emergence of Generative Pre-trained Transformer: version 3 (GPT-3), a pre-trained language model developed by OpenAI, has generated considerable excitement due to its unprecedented ability to generate different solutions. In the realm of timely detecting threats on a password-file, the generation of realistic yet fictitious passwords or honeywords has long been recognized as a crucial aspect of security solutions. However, meeting this requirement has proven to be a persistent challenge. In the face of this crisis, researchers have recently proposed employing GPT-3 as a means to surpass this barrier. This paper presents an analysis of how GPT-3 can potentially undermine the effectiveness of this security solution by accurately distinguishing genuine passwords from a set of honeywords it generates. The experiments conducted for this study reveal that GPT-3 can accurately guess a significant percentage of actual passwords, reaching as high as 53.45% with just three attempts. Though we emphasize the careful use of GPT-3 for generating honeywords, one of the primary findings in this study strongly indicates that GPT-3 can effectively be transformed into an attack mechanism, thus altering the dynamics of the present notion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".