Raw vector
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:N/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XSummary
CVE-2026-26988 is a critical-severity SQL Injection (CWE-89) vulnerability in Librenms Librenms. Its CVSS base score is 9.3 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 6% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
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.
LibreNMS, an auto-discovering PHP/MySQL/SNMP-based network monitoring tool, versions 25.12.0 and below are affected by CVE-2026-26988, an SQL injection vulnerability (CWE-89) in the ajax_table.php endpoint. The flaw occurs because the application fails to properly sanitize or parameterize user input during IPv6 address searches. Specifically, the address parameter is split into an address and prefix, with the prefix portion directly concatenated into the SQL query string without validation, enabling arbitrary SQL command injection. The vulnerability carries a CVSS v3.1 base score of 9.1 (AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:N).
An unauthenticated attacker with network access can exploit this vulnerability with low attack complexity and no user interaction. By supplying a specially crafted IPv6 address in the affected endpoint, the attacker can inject malicious SQL, potentially achieving unauthorized data access or database manipulation, with high impacts to confidentiality and integrity.
The vulnerability has been addressed in LibreNMS version 26.2.0. Mitigation details are available in the GitHub security advisory (https://github.com/librenms/librenms/security/advisories/GHSA-h3rv-q4rq-pqcv), the fixing pull request (https://github.com/librenms/librenms/pull/18777), and the commit (https://github.com/librenms/librenms/commit/15429580baba03ed1dd377bada1bde4b7a1175a1).
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-8039
Vulnerability Data
LibreNMS is an auto-discovering PHP/MySQL/SNMP based network monitoring tool. Versions 25.12.0 and below contain an SQL Injection vulnerability in the ajax_table.php endpoint. The application fails to properly sanitize or parameterize user input when processing IPv6 address searches. Specifically, the address…
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parameter is split into an address and a prefix, and the prefix portion is directly concatenated into the SQL query string without validation. This allows an attacker to inject arbitrary SQL commands, potentially leading to unauthorized data access or database manipulation. This issue has been fixed in version 26.2.0.
- 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.