Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:L/I:L/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-2023-6568 is a medium-severity Cross-site Scripting (CWE-79) vulnerability in Lfprojects Mlflow. Its CVSS base score is 6.1 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Protocol or Service Impersonation (T1001.003); ranked in the top 24% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as Other AI Platforms; in the Other ATLAS/OWASP Terms risk domain.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
A reflected cross-site scripting vulnerability, tracked as CVE-2023-6568, affects the mlflow server component in the mlflow/mlflow repository. The flaw resides in mlflow/server/auth/__init__.py, where user-supplied values from the Content-Type header of POST requests are inserted directly into a Python formatted string and returned in responses without sanitization or escaping, enabling arbitrary JavaScript execution in the victim's browser. The issue carries a CVSS 3.1 score of 6.1 and is classified under CWE-79.
An unauthenticated remote attacker can exploit the vulnerability by crafting a POST request containing malicious JavaScript within the Content-Type header. When a victim interacts with the resulting response, the injected script executes in the context of the mlflow server origin, potentially allowing theft of session tokens, account takeover, or other actions within the victim's browser session.
Public references point to a fix committed in the mlflow repository at 28ff3f94994941e038f2172c6484b65dc4db6ca1, which addresses the unsafe header reflection. The associated huntr.dev bounty report provides additional details on the discovery and remediation.
The EPSS score for this CVE has remained flat at 0.3335 with no material increase observed after disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-0155
Vulnerability Data
A reflected Cross-Site Scripting (XSS) vulnerability exists in the mlflow/mlflow repository, specifically within the handling of the Content-Type header in POST requests. An attacker can inject malicious JavaScript code into the Content-Type header, which is then improperly reflected back to…
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the user without adequate sanitization or escaping, leading to arbitrary JavaScript execution in the context of the victim's browser. The vulnerability is present in the mlflow/server/auth/__init__.py file, where the user-supplied Content-Type header is directly injected into a Python formatted string and returned to the user, facilitating the XSS attack.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Other AI Platforms
- Risk Domain
- Other ATLAS/OWASP Terms
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- MLflow is an open-source platform for managing the end-to-end machine learning lifecycle, including experimentation, reproducibility, and deployment of ML models.
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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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.