Exposed by Default: A Security Analysis of Home Router Default Settings and Beyond
Bibliographic record
Abstract
With the popularity of the Internet, home routers have become crucial for the security of home networks. However, according to the results of our user survey, home routers are often deployed with minimal changes to the factory default settings, which may pose risks to user security and privacy. To systematically evaluate potential risks, we designed a threat-model-based framework and conducted a comprehensive analysis of 40 commercial off-the-shelf home routers from 14 brands. We found a variety of security issues, among which incorrect implementation of TLS is the most common. To improve the efficiency of manually detecting TLS certificate validation vulnerabilities without real routers, we proposed a heuristic method that can narrow down the search scope in firmware and proved its effectiveness with 30 available firmware images of the routers we purchased. Moreover, we evaluated the security of custom remote management protocols and found several cryptographic misuses. Finally, we proposed several recommendations for extending the analysis framework and discussed our ideas about automatically detecting security issues to highlight the need for heightened scrutiny of default settings and inspire other researchers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".