Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/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-2025-27912 is a high-severity CSRF (CWE-352) vulnerability in Datalust Seq. Its CVSS base score is 8.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 8th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to AC-3 (Access Enforcement) and SC-23 (Session Authenticity) — 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-2025-27912 is a cross-site request forgery (CSRF) vulnerability in Datalust Seq versions prior to 2024.3.13545, stemming from missing Content-Type validation. This flaw affects the Seq logging and monitoring server, which supports various authentication methods including Entra ID, OpenID Connect, username/password, and Active Directory. Assigned CWE-352, it carries a CVSS v3.1 base score of 8.8 (AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H), indicating high severity due to its potential for significant confidentiality, integrity, and availability impacts.
The vulnerability can be exploited by an attacker who tricks an authenticated user into visiting a malicious or compromised website. Exploitation is possible in two scenarios: first, when Entra ID or OpenID Connect authentication is enabled, regardless of domain; second, when username/password or Active Directory authentication is used and the malicious site shares the same effective top-level domain (eTLD) as the Seq server. Successful attacks enable impersonation, allowing the attacker to perform arbitrary actions in Seq on behalf of the victim user, such as modifying logs or configurations.
Mitigation requires upgrading to Datalust Seq version 2024.3.13545 or later, as indicated by the vulnerability's versioning in the disclosure. Additional details are available in the vendor's Seq documentation at https://datalust.co/seq and the GitHub issue tracker at https://github.com/datalust/seq-tickets/issues/2366.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-7820
Vulnerability Data
An issue was discovered in Datalust Seq before 2024.3.13545. Missing Content-Type validation can lead to CSRF when (1) Entra ID or OpenID Connect authentication is in use and a user visits a compromised/malicious site, or (2) when username/password or Active…
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Directory authentication is in use and a user visits a compromised/malicious site under the same effective top-level domain as the Seq server. Exploitation of the vulnerability allows the attacker to conduct impersonation attacks and perform actions in Seq on behalf of the targeted user.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V3.3.2V3.5.1V10.2.1
Mitigating Controls (NIST 800-53 r5) AI
Access enforcement requires verifying that state-changing requests originate from the authenticated user rather than a forged cross-site source.
Protecting session authenticity prevents attackers from replaying or forging authenticated requests via the victim's browser.
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 require anti-CSRF controls such as tokens or SameSite attributes.
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.
By denying access to phishing or malicious sites, the control lowers the likelihood that a user will be tricked into submitting a forged request that performs an unintended action on another site.