Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:L/VI:L/VA:L/SC:N/SI:N/SA:N/E:P/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2026-4231 is a medium-severity SSRF (CWE-918) vulnerability. Its CVSS base score is 6.9 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 20th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as NLP and Transformers; in the Supply Chain and Deployment 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-2026-4231 is a server-side request forgery (SSRF) vulnerability affecting vanna-ai vanna versions up to 2.0.2. The issue resides in the update_sql/run_sql functions within the file src/vanna/legacy/flask/__init__.py, part of the Endpoint component. It enables remote manipulation leading to SSRF, as classified under CWE-918, with a CVSS v3.1 base score of 7.3 (AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:L).
Remote attackers require no privileges or user interaction to exploit this vulnerability over the network with low complexity. Successful exploitation allows limited impacts on confidentiality, integrity, and availability, potentially enabling attackers to forge requests from the server to arbitrary destinations, such as internal services or external resources.
Advisories from VulDB note that the vendor was contacted early for disclosure but provided no response, and no patches or mitigations are mentioned. An exploit proof-of-concept is publicly available via a GitHub Gist, increasing the risk of active exploitation.
Vanna-ai is an AI-driven framework for SQL query generation, making this SSRF flaw relevant to AI/ML deployments relying on its legacy Flask endpoints. The public exploit and lack of vendor engagement suggest potential for real-world abuse in exposed instances.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-12376
Vulnerability Data
A vulnerability was found in vanna-ai vanna up to 2.0.2. Affected by this vulnerability is the function update_sql/run_sql of the file src/vanna/legacy/flask/__init__.py of the component Endpoint. Performing a manipulation results in server-side request forgery. The attack may be initiated remotely.…
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The exploit has been made public and could be used. The vendor was contacted early about this disclosure but did not respond in any way.
- CWE(s)
AI Security AnalysisAI
- AI Category
- NLP and Transformers
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai
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.