Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2026-26990 is a high-severity SQL Injection (CWE-89) vulnerability in Librenms Librenms. Its CVSS base score is 8.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 10% 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.
CVE-2026-26990 is a Time-Based Blind SQL Injection vulnerability affecting LibreNMS, an auto-discovering PHP/MySQL/SNMP-based network monitoring tool. The issue resides in the address-search.inc.php file, where the address parameter accepts a crafted subnet prefix that is directly concatenated into an SQL query without proper parameter binding. This flaw impacts versions 25.12.0 and earlier, carrying a CVSS v3.1 base score of 8.8 (AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H) and mapping to CWE-89 (SQL Injection).
Any authenticated user can exploit this vulnerability remotely with low complexity, as it requires only valid credentials to access the affected endpoint. By supplying a malicious subnet prefix, an attacker can manipulate the SQL query logic and infer sensitive database information through time-based conditional responses, such as delays triggered by functions like SLEEP(). The high CVSS impact ratings indicate potential for significant confidentiality, integrity, and availability compromises, including data extraction or modification.
The vulnerability has been addressed in LibreNMS version 26.2.0. Official mitigation details are available in the GitHub security advisory (GHSA-79q9-wc6p-cf92), the fixing pull request (#18777), and the commit (15429580baba03ed1dd377bada1bde4b7a1175a1), which implement proper parameterization to prevent injection. Security practitioners should upgrade to the patched version and review access controls for authenticated users.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-8037
Vulnerability Data
LibreNMS is an auto-discovering PHP/MySQL/SNMP based network monitoring tool. Versions 25.12.0 and below have a Time-Based Blind SQL Injection vulnerability in address-search.inc.php via the address parameter. When a crafted subnet prefix is supplied, the prefix value is concatenated directly into…
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an SQL query without proper parameter binding, allowing an attacker to manipulate query logic and infer database information through time-based conditional responses. This vulnerability requires authentication and is exploitable by any authenticated user. This issue has been fixedd 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.