CVE-2023-36284
SQLi in Webkul Qloapps 1.6.0
Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:NSummary
CVE-2023-36284 is a high-severity SQL Injection (CWE-89) vulnerability in Webkul Qloapps. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 13% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
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-2023-36284 is a time-based SQL injection vulnerability in Webkul QloApps 1.6.0 that resides in the handling of the GET parameters date_from, date_to, and id_product. The issue is tracked under CWE-89 and carries a CVSS 3.1 score of 7.5, reflecting network-accessible, low-complexity exploitation with no required credentials or user interaction and a high impact on confidentiality.
An unauthenticated remote attacker can supply crafted values to these parameters to perform blind SQL injection, bypass the application's authentication and authorization controls, and retrieve arbitrary data from the full database contents. The EPSS score has remained flat at its recorded peak of 0.2655 with no material upward trajectory after disclosure. The supplied references consist solely of the original technical report and contain no advisory statements or patch guidance.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2023-40254
Vulnerability Data
An unauthenticated Time-Based SQL injection found in Webkul QloApps 1.6.0 via GET parameter date_from, date_to, and id_product allows a remote attacker to bypass a web application's authentication and authorization mechanisms and retrieve the contents of an entire database.
- 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
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
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.