Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:NSummary
CVE-2025-53767 is a critical-severity SSRF (CWE-918) vulnerability in Microsoft Azure Openai. Its CVSS base score is 10.0 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 38% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as APIs and Models; in the LLM/Generative AI Risks risk domain.
The strongest mitigations our analysis identified map to AC-4 (Information Flow Enforcement) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
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-2025-53767 is an elevation of privilege vulnerability affecting Azure OpenAI. Published on 2025-08-07, it has a CVSS v3.1 base score of 10.0 (AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:N), marking it as critical severity. The issue maps to CWE-918, server-side request forgery.
Attackers with network access can exploit this vulnerability without requiring privileges, user interaction, or high complexity. Exploitation enables high confidentiality and integrity impacts, with a scope change, potentially allowing privilege escalation within the Azure OpenAI environment.
Microsoft's update guide provides details on the vulnerability at https://msrc.microsoft.com/update-guide/vulnerability/CVE-2025-53767.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-23957
Vulnerability Data
Azure OpenAI Elevation of Privilege Vulnerability
- CWE(s)
AI Security AnalysisAI
- AI Category
- APIs and Models
- Risk Domain
- LLM/Generative AI Risks
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: openai
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.3.6V1.5.3V5.3.2V10.4.7
Mitigating Controls (NIST 800-53 r5) AI
Information flow enforcement can restrict which destinations the server is allowed to contact on behalf of users.
Input validation directly stops untrusted URLs from being accepted and fetched without destination checks.
Boundary protection limits the network reach of server-initiated requests even if SSRF occurs.
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 development practices directly include input validation and destination allow-listing that prevent SSRF.
Runtime monitoring of web applications and services can detect anomalous outbound requests indicative of SSRF.
Vulnerability identification processes can discover and record SSRF flaws in web applications.
Network segmentation and egress controls can limit the damage from successful SSRF requests.
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.
Operational threat data describing SSRF campaigns can be used to tighten outbound-request allow-lists and detection rules before attackers exploit them.