Raw vector
CVSS:3.1/AV:N/AC:H/PR:H/UI:N/S:U/C:H/I:H/A:HCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2023-41899 is a medium-severity SSRF (CWE-918) vulnerability in Home-Assistant Home-Assistant. Its CVSS base score is 6.6 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique LSASS Memory (T1003.001); ranked at the 39th 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
- 🇪🇺 ENISA EUVD: EUVD-2023-46378
Vulnerability Data
Home assistant is an open source home automation. In affected versions the `hassio.addon_stdin` is vulnerable to a partial Server-Side Request Forgery where an attacker capable of calling this service (e.g.: through GHSA-h2jp-7grc-9xpp) may be able to invoke any Supervisor REST…
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API endpoints with a POST request. An attacker able to exploit will be able to control the data dictionary, including its addon and input key/values. This issue has been addressed in version 2023.9.0 and all users are advised to upgrade. There are no known workarounds for this vulnerability. This issue is also tracked as GitHub Security Lab (GHSL) Vulnerability Report: `GHSL-2023-162`.
- CWE(s)
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
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 attempts server-side requests to internal resources, identifying SSRF weaknesses for remediation.
Outbound connections to external resources can be monitored and limited at the boundary, reducing SSRF impact.
Validates server-side URLs and resource references to block SSRF attempts.
Detects server-side request forgery through monitoring of unexpected outbound connections.
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.
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.
Secure development practices directly include input validation and destination allow-listing that prevent SSRF.
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.