Dragonblood: Analyzing the Dragonfly Handshake of WPA3 and EAP-pwd
Bibliographic record
Abstract
The WPA3 certification aims to secure home networks, while EAP-pwd is used by certain enterprise Wi-Fi networks to authenticate users. Both use the Dragonfly handshake to provide forward secrecy and resistance to dictionary attacks. In this paper, we systematically evaluate Dragonfly's security. First, we audit implementations, and present timing leaks and authentication bypasses in EAP-pwd and WPA3 daemons. We then study Dragonfly's design and discuss downgrade and denial-of-service attacks. Our next and main results are side-channel attacks against Dragonfly's password encoding method (e.g. hash-to-curve). We believe that these side-channel leaks are inherent to Dragonfly. For example, after our initial disclosure, patched software was still affected by a novel side-channel leak. We also analyze the complexity of using the leaked information to brute-force the password. For instance, brute-forcing a dictionary of size 1010requires less than $1 in Amazon EC2 instances. These results are also of general interest due to ongoing standardization efforts on Dragonfly as a TLS handshake, Password-Authenticated Key Exchanges (PAKEs), and hash-to-curve. Finally, we discuss backwards-compatible defenses, and propose protocol fixes that prevent attacks. Our work resulted in a new draft of the protocols incorporating our proposed design changes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".