Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:L/A:NSummary
CVE-2024-29889 is a high-severity SQL Injection (CWE-89) vulnerability in Glpi-Project Glpi. Its CVSS base score is 7.1 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 0.9% of CVEs by exploit likelihood; 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.
GLPI, an open-source IT asset management application, contains a SQL injection vulnerability in its saved searches feature that affects all versions prior to 10.0.15. The flaw, tracked as CWE-89, permits an authenticated user to inject arbitrary SQL through the saved-search handling code, resulting in unauthorized modification of other user account records. The issue carries a CVSS 3.1 base score of 7.1.
An authenticated attacker can leverage the injection to alter account data belonging to another user and thereby assume control of that account. Because the attack requires only low-privileged authenticated access and no user interaction, it can be executed remotely over the network with minimal preconditions.
The vulnerability is addressed in GLPI 10.0.15. The project’s security advisory GHSA-8xvf-v6vv-r75g and the associated commit 0a6b28be4c0f848106c60b554c703ec2e178d6c7 document the remediation and confirm that upgrading to the fixed release eliminates the injection vector.
The EPSS score for this CVE reached a peak of 0.7324 and currently stands at 0.6646, indicating sustained exploitation interest after disclosure.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-26873
Vulnerability Data
GLPI is a Free Asset and IT Management Software package. Prior to 10.0.15, an authenticated user can exploit a SQL injection vulnerability in the saved searches feature to alter another user account data take control of it. This vulnerability is…
more
fixed in 10.0.15.
- 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.