Raw vector
CVSS:3.1/AV:L/AC:L/PR:H/UI:N/S:U/C:H/I:N/A:NCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2023-21435 is a medium-severity Exposure of Sensitive Information to an Unauthorized Actor (CWE-200) vulnerability in Samsung Android. Its CVSS base score is 4.4 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique LSASS Memory (T1003.001); ranked at the 7th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-25603
Vulnerability Data
Exposure of Sensitive Information vulnerability in Fingerprint TA prior to SMR Feb-2023 Release 1 allows attackers to access the memory address information via log.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
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.
Monitoring directly detects unauthorized disclosure of sensitive information, enabling response to exposures.
Coordinating audit logging across organizational boundaries reduces the risk of sensitive audit data being exposed to unauthorized actors during transmission.
A data action map identifies locations where sensitive information may be exposed to unauthorized actors during processing or transfer.
The control's identification, isolation, alerting, and eradication steps directly limit the impact and exploitation window of unauthorized sensitive information exposure.
Requiring organization-defined processing conditions on specific PII categories directly reduces the chance that personal data will be exposed to unauthorized actors.
The assessment process surfaces design decisions that could expose sensitive (including PII) data to unauthorized actors, prompting controls that reduce such exposure.
Directly prevents exposure of critical organizational information by applying OPSEC processes across the SDLC.
Filtering output to only permitted content stops unintended disclosure of sensitive information to unauthorized actors.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Log generation configuration can and should exclude sensitive data, but the control statement focuses on availability rather than content filtering.
Secure SDLC practices directly prohibit writing sensitive data to logs; eliminating the weakness satisfies only one narrow slice of the control.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
A.5.10 only requires awareness, a topic-specific acceptable-use policy stating expected behaviours/monitoring, and procedures referencing classification and other controls; per the owner's explicit rule on labelling and similar dependency-style clauses, this does not itself prevent exposure (CWE-200) unless access/DLP/etc. act on it.
Requiring the return of all physical and electronic assets plus secure deletion of organizational data from personal devices reduces the chance that sensitive information remains accessible to departing personnel.
A.5.12 only defines and communicates a classification scheme so that protection needs can be understood; per the owner's explicit rule, labeling/classification does not itself prevent exposure and requires other controls (e.g. access enforcement) to act on it.
By requiring classification labels and metadata on all information assets, the control makes it harder for sensitive data to be inadvertently exposed because downstream systems and users can recognize and enforce handling rules based on those labels.
Requiring encryption, access controls, and recipient authentication for transfers directly reduces the chance that sensitive data reaches an unauthorized observer.
Hardening callouts derived
DISA STIG baselines are authored against specific weaknesses. Where this CVE cites a broad weakness class, the mapping is not asserted at that abstraction (owner ruling, 2026-09-25: grade only at Base and below), so no rule is shown here rather than a rule shown against the wrong grain.