Plug and prey: Exploiting design flaws to hijack EV charging stations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Electric Vehicles (EVs) have become a major element in the global push to combat climate change, given their ability to reduce the transportation sector’s emissions. To support the increasing number of EVs on the road, EV Charging Stations (EVCSs) are being deployed and have become a core element of the transportation infrastructure. EVCSs with individual web portals have been widely studied and proven to be vulnerable to network-based attacks. On the other hand, EVCSs that do not host web portals and cannot be accessed remotely are considered more secure. These EVCSs are generally considered to be more secure and have been overlooked in previous studies. Consequently, in this work, we present the first attack framework that exploits design flaws in this type of EVCS to hijack their operation. Our tests were performed on six actual EVCSs that follow the deployment strategy commonly preferred in North America by most operators and a few operators in Europe. We demonstrate how adversaries can successfully exploit the discussed vulnerabilities to gain unauthorized access to the EVCS configuration and acquire administrator privileges. We then proceed to craft multiple attacks to affect the power grid, steal money, or deteriorate EVCS availability.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it