ChargePrint: A Framework for Internet-Scale Discovery and Security Analysis of EV Charging Management Systems
Bibliographic record
Abstract
Electric Vehicle Charging Management Systems (EVCMS) are a collection of specialized software that allow users to remotely operate Electric Vehicle Charging Stations (EVCS).With the increasing number of deployed EVCS to support the growing global EV fleet, the number of EVCMS are consequently growing, which introduces a new attack surface.In this paper, we propose a novel multi-stage framework, ChargePrint, to discover Internet-connected EVCMS and investigate their security posture.ChargePrint leverages identifiers extracted from a small seed of EVCMS to extend the capabilities of device search engines through iterative fingerprinting and a combination of classification and clustering approaches.Using initial seeds from 1,800 discovered hosts that deployed 9 distinct EVCMS, we identified 27,439 online EVCS instrumented by 44 unique EVCMS.Consequently, our in-depth security analysis highlights the insecurity of the deployed EVCMS by uncovering 120 0day vulnerabilities, which shed light on the feasibility of cyber attacks against the EVCS, its users, and the connected power grid.Finally, while we recommend countermeasures to mitigate future threats, we contribute to the security of the EVCS ecosystem by conducting a Coordinated Vulnerability Disclosure (CVD) effort with system developers/vendors who acknowledged and assigned the discovered vulnerabilities more than 20 CVE-IDs.
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 | 0.000 | 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 | 0.000 | 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 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".