CVE-2026-93474 in App
Summary
by MITRE • 10/02/2026
Charging station authentication identifiers are publicly accessible via web-based mapping platforms.
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Analysis
by VulDB Data Team • 10/02/2026
The exposure of charging station authentication identifiers through public web-based mapping platforms represents a significant security vulnerability that undermines the integrity and access control mechanisms of electric vehicle (EV) infrastructure. These identifiers, which often serve as unique keys or tokens required to initiate charging sessions, are intended to be kept confidential within backend systems and authorized client applications. When such sensitive data is inadvertently exposed via third-party mapping services, it creates a direct pathway for unauthorized actors to exploit the system without needing physical access to the hardware or compromising user credentials through traditional phishing or brute-force attacks. This type of exposure typically stems from poor API security practices on the part of the charging network operator, where authentication endpoints are not properly restricted by IP whitelisting, rate limiting, or strict origin validation, allowing public-facing mapping applications to scrape and display these secrets as if they were standard metadata like location coordinates or connector types.
From a technical perspective, this vulnerability aligns with CWE-200: Exposure of Sensitive Information to an Unauthorized Actor and CWE-798: Use of Hard-coded Credentials, depending on whether the identifiers are static keys embedded in client applications or dynamically generated tokens that have been leaked through insecure data transmission. The operational impact is severe because it allows malicious entities to initiate charging sessions using stolen authentication IDs, leading directly to financial fraud where unauthorized users consume electricity at the expense of legitimate account holders or the service provider. Furthermore, if these identifiers are tied to specific vehicle accounts or payment methods, attackers could potentially link charging activity to individual drivers, compromising user privacy and enabling sophisticated stalking or surveillance campaigns. In more advanced attack scenarios, an adversary might use valid authentication tokens to manipulate charging parameters, such as forcing a rapid charge that degrades battery health over time or disrupting the grid by creating artificial load spikes at specific locations.
This vulnerability also facilitates broader supply chain attacks against EV infrastructure providers. By aggregating large volumes of these identifiers from public mapping platforms, threat actors can build comprehensive databases of active stations and their associated authentication mechanisms. This data enrichment enables targeted phishing campaigns where attackers send convincing emails to fleet managers or individual owners claiming there is an issue with their charging station’s security token, prompting them to reveal additional credentials or install malicious software that exfiltrates further secrets. The ease of access via mapping platforms lowers the barrier to entry for less sophisticated threat actors who might otherwise lack the technical skills required to reverse-engineer proprietary protocols or exploit complex network vulnerabilities in backend systems.
Mitigation strategies must focus on securing the data pipeline between charging infrastructure and third-party services. Charging station operators should implement strict API gateway controls that validate requests based on client certificates, OAuth scopes, and IP reputation scores rather than relying solely on shared secrets exposed to public clients. Authentication identifiers should never be treated as static configuration values; instead, they must be short-lived tokens issued through secure, authenticated channels with robust rotation policies. Additionally, organizations should conduct regular security audits of their API integrations with mapping providers to ensure that sensitive fields are filtered out before data is published publicly. Implementing rate limiting and anomaly detection on authentication endpoints can help identify and block automated scraping attempts in real-time. Finally, adopting a zero-trust architecture for all IoT devices within the charging ecosystem ensures that even if some identifiers are compromised, lateral movement and further exploitation are significantly hindered by requiring continuous verification of device identity and integrity before granting access to critical functions.