Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:H/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-2025-24490 is a critical-severity SQL Injection (CWE-89) vulnerability in Mattermost Mattermost Server. Its CVSS base score is 9.6 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 35th 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 SA-11 (Developer Testing and Evaluation) 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-2025-24490 is a SQL injection vulnerability in Mattermost, affecting versions 10.4.x up to and including 10.4.1, 9.11.x up to and including 9.11.7, 10.3.x up to and including 10.3.2, and 10.2.x up to and including 10.2.2. The flaw stems from the failure to use prepared statements in the SQL query handling boards reordering, enabling attackers to inject malicious SQL payloads when reordering specially crafted boards categories. It has a CVSS v3.1 base score of 9.6 (AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:N) and is associated with CWE-89 (Improper Neutralization of Special Elements used in an SQL Command).
An authenticated attacker with low privileges can exploit this vulnerability remotely over the network with low complexity and no user interaction required. By reordering specially crafted boards categories, the attacker can execute arbitrary SQL queries to retrieve sensitive data from the database (high confidentiality impact) and modify data (high integrity impact), with the attack changing scope to potentially affect higher-privilege components.
For mitigation details, refer to the Mattermost security updates advisory at https://mattermost.com/security-updates.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-4376
Vulnerability Data
Mattermost versions 10.4.x <= 10.4.1, 9.11.x <= 9.11.7, 10.3.x <= 10.3.2, 10.2.x <= 10.2.2 fail to use prepared statements in the SQL query of boards reordering which allows an attacker to retrieve data from the database, via a SQL injection…
more
when reordering specially crafted boards categories.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V6.2.5
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover SQLi flaws before deployment but does not stop their introduction.
Input validation directly stops untrusted data from reaching SQL query construction without neutralization.
Secure engineering principles require parameterized queries and input sanitization that structurally eliminate SQLi.
System monitoring can identify attempted SQLi exploitation via anomalous queries after the weakness exists.
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 injection flaws during coding and review so largely prevent CWE-89 introduction, yet the single broad outcome leaves residual risk from incomplete neutralization techniques or missed edge cases.
Training raises developer awareness of SQLi risks and can reduce introduction likelihood (partial) but removes none of the actual coding flaw's risk by itself since technical neutralization is still required.
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.
The same secure-coding and static-analysis activities surface missing neutralization of SQL metacharacters before the system is accepted.
Early warnings and shared best-practice information help organizations apply the latest remediation techniques against SQL-injection vulnerabilities.
Threat-intelligence feeds that surface new SQL-injection campaigns enable rapid updates to query-construction defenses and detection signatures before exploitation occurs.
Secure-coding rules and security testing phases mandate the use of parameterized queries or equivalent escaping, preventing the construction of dynamic SQL statements from untrusted input.
Language-specific secure coding rules, peer review and SAST together prevent the construction of SQL statements from untrusted data without proper parameterization or escaping.