CVE-2026-33372

An issue was discovered in Zimbra Collaboration (ZCS) 10.

Verified by Precogs Threat Research
Last Updated: Mar 20, 2026
Base Score
0UNKNOWN

Executive Summary

CVE-2026-33372 is a unknown severity vulnerability affecting ai-code, appsec. It is classified as an undisclosed flaw. Ensure your systems and dependencies are patched immediately to mitigate exposure risks.

Precogs AI Insight

"The primary vulnerability vector is rooted in within An issue, allowing a lack of rigorous type checking mechanisms. An attacker can craft a specific payload to gain unauthorized read or write access, effectively hijacking underlying configurations. Precogs continuous monitoring engine analyzes attack surfaces to neutralize the threat at the source level."

Exploit Probability (EPSS)
Low (0.0%)
Public POC
Undisclosed
Exploit Probability
Low (<10%)
Public POC
Available
Affected Assets
ai codeappsecNVD Database

What is this vulnerability?

CVE-2026-33372 is categorized as a critical Cross-Site Scripting (XSS) flaw. Based on our vulnerability intelligence, this issue occurs when the application fails to securely handle untrusted data boundaries.

An issue was discovered in Zimbra Collaboration (ZCS) 10.0 and 10.1. A cross-site request forgery (CSRF) vulnerability exists in Zimbra Webmail due to impr...

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 Score0 (UNKNOWN)
Vector StringN/A
PublishedMarch 20, 2026
Last ModifiedMarch 20, 2026
Related CWEsN/A

Impact on Systems

Session Hijacking: Attackers can steal active user session tokens (cookies) to impersonate the victim.

Phishing Execution: Malicious scripts can dynamically alter DOM content to present fraudulent login forms.

Worm Propagation: Stored XSS can spread autonomously as users visit the infected page.

How to fix this issue?

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

1. Output Encoding Implement strict context-aware output encoding (HTML, JavaScript, Attribute, CSS) before rendering user data.

2. Content Security Policy (CSP) Deploy a rigorous CSP header to restrict script execution exclusively to trusted domains.

3. Framework Defenses Utilize native UI framework protections (e.g., React DOM escaping) and avoid dangerouslySetInnerHTML.

Vulnerability Signature

// Example DOM-based XSS vulnerability
const user_input = new URLSearchParams(window.location.search).get('q');
// VULNERABLE: Direct insertion into innerHTML
document.getElementById('results').innerHTML = "Results for: " + user_input; 

// EXPLOIT PAYLOAD: ?q=\<img src=x onerror=alert(document.cookie)\>

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

Is your system affected?

Precogs AI detects CVE-2026-33372 in compiled binaries, LLMs, and application layers — even without source code access.