Cyber Resilience

CVE-2023-39208

XSS in Zoom ≤ 5.15.10

Published
12 September 2023
Modified
21 November 2024
Patch / advisory
CVSS Score v3.1 6.5
Click a component to see what it means
Raw vectorCVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:L/A:L
EPSS Score 0.0054 42th percentile
Risk Priority 35 floored blend · peak EPSS

Summary

CVE-2023-39208 is a medium-severity Cross-site Scripting (CWE-79) vulnerability in Zoom Zoom. Its CVSS base score is 6.5 (Medium).

Operationally, ranked at the 42th 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

Vulnerability Data

Improper input validation in Zoom Desktop Client for Linux before version 5.15.10 may allow an unauthenticated user to conduct a denial of service via network access.

CWE(s)

Related Threats

CVEs Like This One

CVE-2023-28599Same product: Zoom Zoom
CVE-2023-28598Same product: Zoom Zoom
CVE-2023-34121Same product: Zoom Zoom
CVE-2023-21434Shared CWE-20, CWE-79
CVE-2023-42431Shared CWE-20, CWE-79
CVE-2023-41316Shared CWE-20, CWE-79
CVE-2023-38057Shared CWE-20, CWE-79
CVE-2023-0867Shared CWE-20, CWE-79
CVE-2023-0869Shared CWE-20, CWE-79
CVE-2023-29452Shared CWE-20, CWE-79

Affected Assets

zoom
zoom
≤ 5.15.10

Mitigating Controls

Control response

Prevent
Stop it (NIST 800-53)

Detect
Catch it (NIST detect / respond)

Harden
Shrink the surface (DISA STIG)
  • 6 hardening rules · 3 OS baselines
Validate
Prove the fix (OWASP ASVS)
  • V1.1.2
  • V1.3.2

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.

addresses: CWE-20 CWE-79

Directly implements checks on information inputs to reject invalid data before processing.

addresses: CWE-79

Penetration testing submits XSS payloads to web applications, detecting cross-site scripting flaws for subsequent remediation.

addresses: CWE-20

Security testing and developer training directly verify and enforce proper input validation, reducing exploitability of injection and malformed-data weaknesses.

addresses: CWE-20

Security testing and evaluation at multiple SDLC stages directly detects missing or flawed input validation, with the required remediation process ensuring fixes are applied.

addresses: CWE-79

Output validation against expected content can reject or sanitize script content in generated web pages, reducing XSS exploitability.

addresses: CWE-20

Spam protection mechanisms perform filtering and detection on inbound/outbound messages, directly compensating for missing or weak input validation of unsolicited content.

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.

PR.PS-06 mostly match
prevents

Secure SDLC practices directly target introduction of XSS via coding standards/testing (mostly), yet the single broad outcome leaves many specific neutralization vectors unaddressed (partial).

PR.PS-02 partial match
prevents

Patching and EOL replacement can remediate known XSS instances in libraries or frameworks (partial) but do nothing to enforce input neutralization in application code (none).

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.

detects

Secure-coding testing and automated code-analysis tools are applied to detect improper neutralization of script-related content during web-page generation.

prevents

Knowledge exchange on emerging attack techniques and patches reduces the likelihood that cross-site scripting flaws remain unaddressed in deployed applications.

prevents

Operational indicators of compromise for web-application attacks can be incorporated into WAF or input-filtering rules, lowering the likelihood that unsanitized data reaches the browser.

prevents

Requiring language-specific secure-coding standards and automated scanning during the SDLC catches missing output encoding or improper neutralization of untrusted data before the software reaches production.

prevents

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.

prevents

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.

References