Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:LSummary
CVE-2024-54145 is a medium-severity SQL Injection (CWE-89) vulnerability in Cacti Cacti. Its CVSS base score is 6.3 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 49th percentile by exploit likelihood (below the median); 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-2024-54145 is a SQL injection vulnerability (CWE-89) in Cacti, an open source performance and fault management framework. The flaw exists in the get_discovery_results function within automation_devices.php, where the network parameter is not properly sanitized, allowing injection of malicious SQL queries. Published on 2025-01-27, it carries a CVSS v3.1 base score of 6.3 (AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:L) and was fixed in Cacti version 1.2.29.
An attacker requires low privileges, such as those of an authenticated user, to exploit this over the network with low complexity and no user interaction. Successful exploitation enables limited impacts: partial disclosure of sensitive data (low confidentiality), modification of underlying data (low integrity), and limited denial of service (low availability) through arbitrary SQL execution.
Mitigation is available via upgrade to Cacti 1.2.29, as detailed in the fixing commit at https://github.com/Cacti/cacti/commit/c7e4ee798d263a3209ae6e7ba182c7b65284d8f0 and the GitHub Security Advisory GHSA-fh3x-69rr-qqpp at https://github.com/Cacti/cacti/security/advisories/GHSA-fh3x-69rr-qqpp. Debian LTS users should refer to the announcement at https://lists.debian.org/debian-lts-announce/2025/02/msg00010.html for package-specific patches and guidance.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-52313
Vulnerability Data
Cacti is an open source performance and fault management framework. Cacti has a SQL injection vulnerability in the get_discovery_results function of automation_devices.php using the network parameter. This vulnerability is fixed in 1.2.29.
- 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.