Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2024-6457 is a critical-severity SQL Injection (CWE-89) vulnerability in Pluginus Husky - Products Filter Professional For Woocommerce. Its CVSS base score is 9.8 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 3% 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.
The HUSKY – Products Filter Professional for WooCommerce plugin for WordPress is vulnerable to time-based SQL injection in all versions through 1.3.6. The flaw exists in the woof_author parameter, where user input is insufficiently escaped and the existing SQL query lacks prepared-statement handling, allowing an attacker to append arbitrary SQL.
Unauthenticated remote attackers can exploit the issue over the network to extract sensitive database contents. The vulnerability carries a CVSS 3.1 score of 9.8, reflecting full impact on confidentiality, integrity, and availability with no authentication or user interaction required.
Public references point to a fix committed in WordPress plugin changeset 3116888 and documented in the Wordfence advisory for ID ecfdf7b1-9bb8-4c1d-a00a-ca1e44440cab; administrators should update to a patched release or apply the changeset to close the injection vector. The associated EPSS score remains flat at 0.0848 with no material post-disclosure rise.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-47553
Vulnerability Data
The HUSKY – Products Filter Professional for WooCommerce plugin for WordPress is vulnerable to time-based SQL Injection via the ‘woof_author’ parameter in all versions up to, and including, 1.3.6 due to insufficient escaping on the user supplied parameter and lack…
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of sufficient preparation on the existing SQL query. This makes it possible for unauthenticated attackers to append additional SQL queries into already existing queries that can be used to extract sensitive information from the 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
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.