CVE-2025-69243

Raytha CMS is vulnerable to User Enumeration in password reset functionality.

Verified by Precogs Threat Research
Last Updated: Mar 16, 2026
Base Score
5.3MEDIUM

Executive Summary

CVE-2025-69243 is a medium severity vulnerability affecting appsec. It is classified as CWE-204. Ensure your systems and dependencies are patched immediately to mitigate exposure risks.

Precogs AI Insight

"This critical flaw stems from within Raytha CMS, allowing a failure to enforce strict data boundary conditions. This flaw provides a direct pathway for attackers to inject malicious logic that alters the execution flow of the application engine. Precogs AI Analysis Engine utilizes semantic code analysis to identify exploitable weaknesses before attackers do."

Exploit Probability (EPSS)
Low (0.0%)
Public POC
Undisclosed
Exploit Probability
Low (<10%)
Public POC
Available
Affected Assets
appsecCWE-204

What is this vulnerability?

CVE-2025-69243 is categorized as a critical Application Verification Flaw flaw. Based on our vulnerability intelligence, this issue occurs when the application fails to securely handle untrusted data boundaries.

Raytha CMS is vulnerable to User Enumeration in password reset functionality. Difference in messages could allow an attacker to determine if the login is v...

This architectural defect enables adversaries to bypass intended security controls, directly manipulating the application's execution state or data layer. Immediate strategic intervention is required.

Risk Assessment

MetricValue
CVSS Base Score5.3 (MEDIUM)
Vector StringCVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N
PublishedMarch 16, 2026
Last ModifiedMarch 16, 2026
Related CWEsCWE-204

Impact on Systems

Unauthorized Access: Flaws in application logic can permit unauthorized interaction with protected APIs.

Data Manipulation: Adversaries may alter critical application states, such as user roles or configurations.

Service Disruption: Improper error handling or unvalidated inputs can lead to resource exhaustion.

How to fix this issue?

Implement the following strategic mitigations immediately to eliminate the attack surface.

1. Defense in Depth Implement multi-layered validation (client-side, API gateway, and server-side).

2. Least Privilege Ensure backend service accounts operate with the absolute minimum rights required.

3. Security Regression Testing Integrate automated semantic security scanning into the deployment pipeline.

Vulnerability Signature

// Generic Application Security Flaw (Node.js)
app.post('/api/update-profile', (req, res) =\> \{
    // DANGEROUS: Mass Assignment / Object Injection
    // Attacker can pass \{ "isAdmin": true, "email": "..." \}
    User.update(\{ id: req.user.id \}, req.body);
    
    // SECURED: Explicitly select permitted fields
    const \{ email, displayName, bio \} = req.body;
    User.update(\{ id: req.user.id \}, \{ email, displayName, bio \});
\});

References and Sources

Vulnerability Code Signature

Attack Data Flow

StageDetail
SourceUntrusted User Input
VectorInput flows through the application logic without sanitization
SinkExecution or Rendering Sink
ImpactApplication compromise, Logic Bypass, Data Exfiltration

Vulnerable Code Pattern

# ❌ VULNERABLE: Unsanitized Input Flow
def process_request(request):
    user_input = request.GET.get('data')
    # Taint sink: processing untrusted data
    execute_logic(user_input)
    return {"status": "success"}

Secure Code Pattern

# ✅ SECURE: Input Validation & Sanitization
def process_request(request):
    user_input = request.GET.get('data')
    
    # Sanitized boundary check
    if not is_valid_format(user_input):
        raise ValueError("Invalid input format")
        
    sanitized_data = sanitize(user_input)
    execute_logic(sanitized_data)
    return {"status": "success"}

How Precogs Detects This

Precogs AI Analysis Engine maps untrusted input directly to execution sinks to catch complex application security vulnerabilities.\n

Related Vulnerabilitiesvia CWE-204

Is your system affected?

Precogs AI detects CVE-2025-69243 in compiled binaries, LLMs, and application layers — even without source code access.