CVE-2025-1740 Overview
CVE-2025-1740 is an Improper Restriction of Excessive Authentication Attempts vulnerability (CWE-307) affecting Akinsoft MyRezzta restaurant management software. This critical flaw allows attackers to bypass authentication mechanisms through brute force attacks and password recovery exploitation. The vulnerability stems from the application's failure to implement adequate rate limiting or account lockout mechanisms, enabling unlimited authentication attempts against target accounts.
Critical Impact
This vulnerability allows unauthenticated remote attackers to gain unauthorized access to MyRezzta systems through brute force attacks, potentially compromising sensitive business data and customer information.
Affected Products
- Akinsoft MyRezzta versions from s2.03.01 before v2.05.01
Discovery Timeline
- 2025-09-03 - CVE-2025-1740 published to NVD
- 2025-09-04 - Last updated in NVD database
Technical Details for CVE-2025-1740
Vulnerability Analysis
This vulnerability represents a fundamental authentication security flaw where the Akinsoft MyRezzta application fails to implement proper restrictions on repeated authentication attempts. Without rate limiting, account lockout policies, or CAPTCHA mechanisms, the application permits unlimited login attempts from any source. This enables attackers to systematically attempt credential combinations until valid credentials are discovered.
The attack can be conducted entirely over the network without requiring any prior authentication or user interaction, making it particularly dangerous for internet-exposed deployments. Successful exploitation grants attackers full access to the compromised account with whatever privileges that account possesses.
Root Cause
The root cause is the absence of defensive mechanisms against automated authentication attempts in the MyRezzta application. Specifically, the application lacks:
- Rate limiting on login endpoints
- Progressive delays after failed authentication attempts
- Account lockout policies after consecutive failures
- CAPTCHA or challenge-response mechanisms
- IP-based blocking for suspicious activity
This design oversight allows attackers to leverage automated tools to test thousands of credential combinations without triggering any defensive responses from the application.
Attack Vector
The attack vector is network-based, allowing remote exploitation without authentication. Attackers can target the MyRezzta login interface with automated credential stuffing or dictionary attacks. The vulnerability also enables exploitation of password recovery mechanisms, potentially allowing attackers to enumerate valid accounts or bypass recovery workflows.
Attack scenarios include:
- Brute Force Login: Automated tools systematically attempt username/password combinations
- Credential Stuffing: Using leaked credential databases from other breaches against MyRezzta accounts
- Password Recovery Exploitation: Abusing the password reset functionality to gain unauthorized access or enumerate valid accounts
Detection Methods for CVE-2025-1740
Indicators of Compromise
- High volume of failed login attempts from single or multiple IP addresses targeting MyRezzta authentication endpoints
- Unusual patterns in password reset requests, particularly bulk requests or requests for multiple accounts
- Successful logins from geographic locations inconsistent with normal user access patterns
- Authentication logs showing rapid sequential login attempts against single or multiple user accounts
Detection Strategies
- Implement log monitoring for authentication endpoints to detect anomalous login attempt volumes
- Configure SIEM alerts for excessive failed authentication events within defined time windows
- Deploy web application firewall (WAF) rules to detect and block brute force attack patterns
- Monitor for credential stuffing indicators such as login attempts using known breached credentials
Monitoring Recommendations
- Enable verbose authentication logging in MyRezzta to capture source IP, timestamp, and attempt outcome
- Establish baseline metrics for normal authentication patterns to identify statistical anomalies
- Implement real-time alerting for login failure rates exceeding established thresholds
- Review password reset request logs regularly for suspicious activity patterns
How to Mitigate CVE-2025-1740
Immediate Actions Required
- Update Akinsoft MyRezzta to version v2.05.01 or later immediately
- Implement network-level rate limiting on authentication endpoints using a reverse proxy or WAF
- Enable multi-factor authentication (MFA) for all user accounts where supported
- Audit authentication logs for evidence of prior exploitation attempts
- Reset credentials for any accounts showing suspicious login patterns
Patch Information
Akinsoft has addressed this vulnerability in MyRezzta version v2.05.01. Organizations running affected versions (s2.03.01 through versions prior to v2.05.01) should upgrade immediately. For additional details, refer to the USOM Security Notification TR-25-0205.
Workarounds
- Deploy a web application firewall (WAF) in front of MyRezzta with brute force protection rules enabled
- Implement IP-based rate limiting at the network or reverse proxy layer to restrict authentication attempts per IP address
- Configure geographic IP restrictions to limit access to MyRezzta from authorized regions only
- Place MyRezzta behind a VPN to restrict access to authenticated internal users
- Implement CAPTCHA mechanisms at the network edge if the application does not natively support them
# Example nginx rate limiting configuration for authentication endpoints
# Add to nginx server configuration
# Define rate limiting zone (10 requests per second per IP)
limit_req_zone $binary_remote_addr zone=auth_limit:10m rate=10r/s;
# Apply to authentication endpoints
location /login {
limit_req zone=auth_limit burst=20 nodelay;
limit_req_status 429;
proxy_pass http://myrezzta_backend;
}
location /password-reset {
limit_req zone=auth_limit burst=5 nodelay;
limit_req_status 429;
proxy_pass http://myrezzta_backend;
}
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.

