CVE-2025-66035
Published: 26 November 2025
Summary
CVE-2025-66035 is a high-severity Insertion of Sensitive Information Into Sent Data (CWE-201) vulnerability in Siemens (inferred from references). Its CVSS base score is 7.7 (High).
Operationally, ranked at the 40.6th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-199769
Vulnerability details
Angular is a development platform for building mobile and desktop web applications using TypeScript/JavaScript and other languages. Prior to versions 19.2.16, 20.3.14, and 21.0.1, there is a XSRF token leakage via protocol-relative URLs in angular HTTP clients. The vulnerability is…
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a Credential Leak by App Logic that leads to the unauthorized disclosure of the Cross-Site Request Forgery (XSRF) token to an attacker-controlled domain. Angular's HttpClient has a built-in XSRF protection mechanism that works by checking if a request URL starts with a protocol (http:// or https://) to determine if it is cross-origin. If the URL starts with protocol-relative URL (//), it is incorrectly treated as a same-origin request, and the XSRF token is automatically added to the X-XSRF-TOKEN header. This issue has been patched in versions 19.2.16, 20.3.14, and 21.0.1. A workaround for this issue involves avoiding using protocol-relative URLs (URLs starting with //) in HttpClient requests. All backend communication URLs should be hardcoded as relative paths (starting with a single /) or fully qualified, trusted absolute URLs.
- CWE(s)
Related Threats
No named actor attribution yet. ATT&CK technique mapping in progress for this CVE.
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Embedding taints allows detection when sensitive data is inserted into outbound or sent data streams.
Automated marking identifies private personal information in outputs, tangibly reducing the ability to exploit weaknesses that result in its unauthorized exposure.
Privacy-specific attributes and their controlled association directly reduce exposure of private personal information through missing or incorrect labeling.
Preventing nonpublic personal information from public posting reduces unauthorized exposure of private personal data.
The control detects and protects against mining of private personal information, reducing unauthorized exposure of PII.
Privacy literacy training directly targets preventing exposure of personal information through user mishandling.
Tracking locations of sensitive data and access users reduces risk of private personal information exposure.
PIA explicitly identifies PII collection/use/disclosure flows and drives mitigations that reduce the likelihood of unauthorized exposure of private personal information.