Fact-Checking 5G Security: Bridging the Gap Between Expectations and Reality
Bibliographic record
Abstract
5G cellular systems are currently being deployed worldwide delivering the promised unprecedented levels of throughput and latency to hundreds of millions of users. At such scale and reach, security is crucial. Consequently, the 5G standard includes a new series of features to improve the security of its predecessors (i.e., 3G and 4G). In this work, we evaluate the security of currently deployed 5G commercial networks in Europe and North America. Specifically, by collecting 5G signaling traffic in the wild in several cities in Spain, Germany, France, Canada, and the USA, we i) fact-check which 5G security enhancements are implemented in current deployments, ii) provide a rich overview of the implementation status of each 5G security feature in a selection of 5G commercial networks in Europe and North America and compare it with previous results in China, iii) analyze the implications of optional features not being deployed, and iv) discuss on the still remaining 4G-inherited vulnerabilities. Our findings indicate that the rollout of 5G security features in the analyzed commercial networks is still a work in progress. On the one hand, several networks continue to rely on 4G for their core network operations, which hinders the deployment of new security features (e.g., SUCI) and, on the other hand, fully-fledged 5G deployments lack mandatory security measures such as GUTI reallocation after paging. Moreover, we find that some operators fail to provide proper temporary identifier randomization, in both 4G and 5G networks. Some of the obtained results are aligned with results previously reported from China <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> and keep the European and North American studied networks vulnerable to some 4G attacks, during their migration period from 4G to 5G. Conversely, studied networks deployed in North America exhibit stronger adherence to 5G security standards, with near-complete compliance observed, in contrast to deployments in China and Europe, where comparatively lower compliance levels have been observed.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.007 | 0.002 |
| 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".