Raw vector
CVSS:3.1/AV:N/AC:L/PR:H/UI:R/S:U/C:N/I:H/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-2023-36877 is a medium-severity Cross-site Scripting (CWE-79) vulnerability in Microsoft Azure Hdinsight. Its CVSS base score is 4.5 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Protocol or Service Impersonation (T1001.003); ranked in the top 37% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2023-36877 is a spoofing vulnerability affecting Azure Apache Oozie, assigned CWE-79 and carrying a CVSS 3.1 base score of 4.5. The flaw permits an attacker to inject or alter content presented to users through the affected Azure-hosted Oozie component, consistent with cross-site scripting behavior that impacts integrity without affecting confidentiality or availability.
Exploitation requires an authenticated attacker with high privileges who can also induce a victim user to interact with a maliciously crafted request over the network. Successful abuse allows the attacker to spoof content or perform unauthorized actions within the Oozie interface while remaining under the privileges of the targeted user.
Microsoft’s advisory at the MSRC update guide recommends applying the patches or configuration updates listed for the affected Azure Apache Oozie deployments to remediate the issue.
The EPSS score rose materially from a low baseline to a peak of 0.0741 on 2025-01-22 before receding, indicating a period of increased exploitation interest after public disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-40797
Vulnerability Data
Azure Apache Oozie Spoofing Vulnerability
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
—
—
—
V1.1.2V1.3.2
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.
Penetration testing submits XSS payloads to web applications, detecting cross-site scripting flaws for subsequent remediation.
Validates web inputs to reject script-related content that could produce XSS.
Output validation against expected content can reject or sanitize script content in generated web pages, reducing XSS exploitability.
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.
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).
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.
A.5.6 only promotes membership in groups to improve knowledge, receive alerts, and share information; it performs no action that stops XSS exploitation (or even the underlying neutralization defect) and is a pure dependency for other controls per the owner's labeling rule.