Security Resilience

CVE-2024-10499

SQLi in Meowapps Ai Engine ≤ 2.6.5

Public PoCSQLi
Published
12 December 2024
Modified
17 May 2025
CVSS Score v3.1 7.2
Click a component to see what it means
Raw vectorCVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H
EPSS Score 0.0061 48th percentile
Risk Priority 54 floored blend · peak EPSS

CVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.

Summary

CVE-2024-10499 is a high-severity SQL Injection (CWE-89) vulnerability in Meowapps Ai Engine. Its CVSS base score is 7.2 (High).

Operationally, exploitation aligns with the MITRE ATT&CK technique Protocol or Service Impersonation (T1001.003); ranked at the 48th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.

This vulnerability is AI-related — categorised as LLM Application Platforms; in the Other ATLAS/OWASP Terms risk domain.

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.

OWASP Top 10 for Web (2025)

A05:2025 Injection

EU & UK References

Vulnerability Data

The AI Engine WordPress plugin before 2.6.5 does not sanitize and escape a parameter from one of its RESP API endpoint before using it in a SQL statement, allowing admins to perform SQL injection attacks

CWE(s)
CWE-89

AI Security AnalysisAI

AI Category
LLM Application Platforms
Risk Domain
Other ATLAS/OWASP Terms
OWASP Top 10 for LLMs 2025
None mapped
Classification Reason
The vulnerability affects the AI Engine WordPress plugin, which provides AI features like chatbots and discussions, fitting as an enterprise AI assistant integration for WordPress sites.

Related Threats

MITRE ATT&CK Enterprise Techniques

T1001.003 Protocol or Service Impersonation Command And Control
Adversaries may impersonate legitimate protocols or web service traffic to disguise command and control activity and thwart analysis efforts.
attack.mitre.org →
T1003 OS Credential Dumping Credential Access
Adversaries may attempt to dump credentials to obtain account login and credential material, normally in the form of a hash or a clear text password.
attack.mitre.org →
T1003.001 LSASS Memory Credential Access
Adversaries may attempt to access credential material stored in the process memory of the Local Security Authority Subsystem Service (LSASS).
attack.mitre.org →
T1003.002 Security Account Manager Credential Access
Adversaries may attempt to extract credential material from the Security Account Manager (SAM) database either through in-memory techniques or through the Windows Registry where the SAM database is stored.
attack.mitre.org →
T1003.003 NTDS Credential Access
Adversaries may attempt to access or create a copy of the Active Directory domain database in order to steal credential information, as well as obtain other information about domain members such as devices, users, and access rights.
attack.mitre.org →
T1005 Data from Local System Collection
Adversaries may search local system sources, such as file systems, configuration files, local databases, virtual machine files, or process memory, to find files of interest and sensitive data prior to Exfiltration.
attack.mitre.org →
Derived from this CVE’s CWE(s) via the direct CWE→ATT&CK cross-walk.

CVEs Like This One

CVE-2024-6723Same product: Meowapps Ai Engine
CVE-2024-5753Shared CWE-89
CVE-2023-48741Shared CWE-89
CVE-2023-36189Shared CWE-89
CVE-2024-7042Shared CWE-89
CVE-2023-4899Shared CWE-89
CVE-2023-3686Shared CWE-89
CVE-2024-7456Shared CWE-89
CVE-2023-48687Shared CWE-89
CVE-2023-30092Shared CWE-89

Affected Assets

meowapps
ai engine
≤ 2.6.5

Mitigating Controls

Control response

Prevent
Stop it (NIST 800-53)

—

Detect
Catch it (NIST detect / respond)

—

Harden
Shrink the surface (DISA STIG)

—

Validate
Prove the fix (OWASP ASVS)
  • V6.2.5

Mitigating Controls (NIST 800-53 r5) AI

SA-11 Developer Testing and Evaluation good match

Developer testing and evaluation can discover SQLi flaws before deployment but does not stop their introduction.

SI-10 Information Input Validation good match

Input validation directly stops untrusted data from reaching SQL query construction without neutralization.

SA-8 Security and Privacy Engineering Principles partial match

Secure engineering principles require parameterized queries and input sanitization that structurally eliminate SQLi.

SI-4 System Monitoring partial match

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.

PR.PS-06 mostly match
blocks

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.

PR.AT-02 partial match
blocks

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.

A.5.14 Information transfer partial match
removes
A.5.15 Access control partial match
removes
A.5.16 Identity management partial match
removes
A.5.17 Authentication information partial match
removes
A.5.18 Access rights partial match
removes
A.5.6 Contact with special interest groups partial match
blocks

A.5.6 only improves knowledge flow, awareness of threats/patches and access to specialist advice; it does not stop SQL injection from being exploitable in code that still contains the flaw.

References