CVE-2023-33182
Nextcloud Contacts 4.1.0 – 4.2.4
Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/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-2023-33182 is a uncategorised-severity Improper Input Validation (CWE-20) vulnerability in Nextcloud Contacts. Its CVSS base score is 0.0.
Operationally, ranked in the top 43% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-37365
Vulnerability Data
Contacts app for Nextcloud easily syncs contacts from various devices with your Nextcloud and allows editing. The unsanitized SVG is converted to a JavaScript blob (in memory data) that the Avatar can't render. Due to this constellation the missing sanitization…
more
does not seem to be exploitable. It is recommended that the Contacts app is upgraded to 5.0.3 or 4.2.4
- CWE(s)
Related Threats
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.
Security testing and developer training directly verify and enforce proper input validation, reducing exploitability of injection and malformed-data weaknesses.
Security testing and evaluation at multiple SDLC stages directly detects missing or flawed input validation, with the required remediation process ensuring fixes are applied.
Directly implements checks on information inputs to reject invalid data before processing.
Spam protection mechanisms perform filtering and detection on inbound/outbound messages, directly compensating for missing or weak input validation of unsolicited content.
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.8.25 mandates security requirements, secure coding guidelines, checkpoints, testing (incl. code scans) and developer training in the SDLC, which reaches a slice of input validation defects but leaves the majority (incomplete enforcement, second-order inputs, parser differentials, post-validation canonicalization) unaddressed as in the SA-3 and A.8.28 anchors.
Mandating input controls that include integrity checks and input validation ensures that untrusted data is examined before use, blocking the root cause of many injection and malformed-data weaknesses.
Security-by-design principles explicitly call for data validation and sanitization at every layer, reducing the chance that malformed or malicious input will be processed without scrutiny.
Requiring language-specific secure coding standards, peer review, SAST and documented mitigation of common programming errors forces validation of all inputs before they are trusted.
Testing against a defined set of requirements and using code review plus vulnerability scanning forces validation of inputs and handling of unanticipated conditions, reducing the chance that malformed data will be accepted.
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.
- No fine-grained STIG row for CWE-20, a class-level weakness, and none of its children carries one either.