CVE-2026-38528: Krayin CRM SQL Injection Vulnerability

CVE-2026-38528 Overview

A SQL injection vulnerability has been discovered in Krayin CRM v2.2.x that allows attackers to execute arbitrary SQL commands through the rotten_lead parameter in the /Lead/LeadDataGrid.php endpoint. This vulnerability enables authenticated attackers to manipulate database queries, potentially leading to unauthorized access to sensitive customer relationship management data.

Critical Impact

Authenticated attackers can exploit this SQL injection flaw to extract sensitive CRM data including customer information, sales records, and business intelligence data stored in the Krayin CRM database.

Affected Products

• Krayin CRM v2.2.x
• Krayin Laravel CRM (open-source editions running vulnerable versions)

Discovery Timeline

• 2026-04-14 - CVE-2026-38528 published to NVD
• 2026-04-14 - Last updated in NVD database

Technical Details for CVE-2026-38528

Vulnerability Analysis

This SQL injection vulnerability (CWE-89) exists in the Lead management module of Krayin CRM, specifically within the LeadDataGrid.php component. The vulnerability stems from improper sanitization of the rotten_lead parameter, which is used to filter lead data in the CRM's data grid interface.

SQL injection vulnerabilities in CRM systems are particularly dangerous because they provide direct access to databases containing sensitive customer information, sales data, and business intelligence. In this case, an authenticated user with access to the Lead module can craft malicious SQL queries that bypass intended access controls.

The network-accessible nature of this vulnerability combined with low attack complexity makes it exploitable by any authenticated user who can reach the affected endpoint. While user interaction is not required, the attacker must have valid credentials to access the Lead module where the vulnerable parameter resides.

Root Cause

The root cause of this vulnerability is improper input validation and sanitization of the rotten_lead parameter before it is incorporated into SQL queries. The application fails to properly escape or parameterize user-supplied input, allowing SQL metacharacters to modify the intended query structure.

This represents a classic SQL injection pattern where dynamic SQL construction using untrusted input enables query manipulation. The Laravel framework provides built-in protection mechanisms such as parameterized queries and Eloquent ORM, but these protections were not properly implemented for this specific parameter.

Attack Vector

The attack vector is network-based, requiring authenticated access to the Krayin CRM application. An attacker would need to:

1. Authenticate to the Krayin CRM application with valid credentials
2. Navigate to or directly access the Lead module endpoint at /Lead/LeadDataGrid.php
3. Inject malicious SQL payloads through the rotten_lead parameter
4. Extract data or perform unauthorized database operations based on database permissions

The vulnerability allows for high confidentiality impact, enabling extraction of sensitive data from the CRM database. There is also potential for limited integrity impact through data manipulation. Technical details and proof-of-concept information are available in the GitHub Security Advisory for CVE-2026-38528.

Detection Methods for CVE-2026-38528

Indicators of Compromise

• Unusual SQL error messages in application logs referencing the LeadDataGrid.php file or rotten_lead parameter
• Anomalous database queries containing SQL injection patterns such as UNION SELECT, OR 1=1, or time-based payloads
• Unexpected data access patterns or bulk data extraction from lead-related database tables
• Web application firewall alerts for SQL injection attempts targeting CRM endpoints

Detection Strategies

• Implement web application firewall (WAF) rules to detect and block SQL injection patterns in requests to /Lead/LeadDataGrid.php
• Enable verbose database query logging to identify malformed or suspicious queries originating from the Lead module
• Deploy application-layer intrusion detection to monitor for SQL metacharacters in the rotten_lead parameter
• Review access logs for repeated requests to the LeadDataGrid endpoint with varying parameter values

Monitoring Recommendations

• Configure alerting on SQL error rates and patterns in application logs
• Monitor database query execution times for anomalies that may indicate time-based blind SQL injection attempts
• Implement user behavior analytics to detect unusual data access patterns within the CRM Lead module
• Set up real-time monitoring for outbound data exfiltration attempts following potential SQL injection exploitation

How to Mitigate CVE-2026-38528

Immediate Actions Required

• Restrict access to the Lead module to only essential personnel until patching is complete
• Implement WAF rules to filter SQL injection payloads targeting the rotten_lead parameter
• Review database permissions to ensure the CRM application user has minimal required privileges
• Enable database query logging and monitoring for suspicious activity

Patch Information

Organizations using Krayin CRM v2.2.x should check the official Krayin Laravel CRM repository for security updates and patched versions. It is recommended to upgrade to the latest stable release that addresses this SQL injection vulnerability.

Workarounds

• Deploy a web application firewall with SQL injection detection rules in front of the Krayin CRM application
• Implement input validation at the network edge to sanitize the rotten_lead parameter before it reaches the application
• If feasible, temporarily disable or restrict access to the Lead module's data grid functionality until a patch is applied
• Consider implementing additional authentication requirements for accessing sensitive CRM endpoints

# Example WAF rule for ModSecurity to block SQL injection on the vulnerable parameter
SecRule ARGS:rotten_lead "@detectSQLi" \
    "id:100001,\
    phase:2,\
    deny,\
    status:403,\
    msg:'SQL Injection attempt detected in rotten_lead parameter - CVE-2026-38528',\
    log,\
    auditlog"