CVE-2025-3035
Published: 01 April 2025
Summary
CVE-2025-3035 is a medium-severity Exposure of Private Personal Information to an Unauthorized Actor (CWE-359) vulnerability in Mozilla Firefox. Its CVSS base score is 5.3 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Application Window Discovery (T1010); ranked at the 34.6th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Other Platforms; in the Privacy and Disclosure risk domain.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-9295
Vulnerability details
By first using the AI chatbot in one tab and later activating it in another tab, the document title of the previous tab would leak into the chat prompt. This vulnerability was fixed in Firefox 137.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Other Platforms
- Risk Domain
- Privacy and Disclosure
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
The vulnerability enables leakage of document titles from a previously used AI chatbot tab into a new tab's chat prompt, facilitating Application Window Discovery (T1010) by revealing open tab/window information and Browser Information Discovery (T1217) by disclosing browser tab contents and user activity.
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.
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.
The control specifically requires architectures that minimize privacy risk when processing PII, directly addressing exposure of personal information.