CVE-2026-24903: OrcaStatLLM Researcher XSS Vulnerability

CVE-2026-24903 Overview

A Stored Cross-Site Scripting (XSS) vulnerability has been identified in OrcaStatLLM-Researcher, an LLM-based research paper generator. The vulnerability exists in the Log Message functionality within the Session Page, allowing attackers to inject and execute arbitrary JavaScript code in victims' browsers through malicious research topic inputs.

Critical Impact

Attackers can inject persistent malicious scripts that execute in the context of other users' browser sessions, potentially leading to session hijacking, credential theft, or unauthorized actions on behalf of authenticated users.

Affected Products

• OrcaStatLLM-Researcher (Session Page - Log Message functionality)

Discovery Timeline

• 2026-02-06 - CVE-2026-24903 published to NVD
• 2026-02-06 - Last updated in NVD database

Technical Details for CVE-2026-24903

Vulnerability Analysis

This vulnerability is classified as CWE-79 (Improper Neutralization of Input During Web Page Generation), commonly known as Cross-Site Scripting. The Stored XSS variant is particularly dangerous because the malicious payload persists in the application's storage and executes whenever a user views the affected page.

The vulnerability resides in the Session Page's Log Message feature where user-supplied research topic inputs are not properly sanitized before being rendered in the browser. When an attacker submits a crafted research topic containing JavaScript code, the application stores this input without adequate encoding or validation. Subsequently, when any user views the session logs, the malicious script executes within their browser context.

Root Cause

The root cause of this vulnerability is insufficient input validation and output encoding in the OrcaStatLLM-Researcher application. The Log Message component fails to properly sanitize or escape user-controlled research topic data before storing it in the database and rendering it back to users in the Session Page. This lack of proper encoding allows HTML and JavaScript content to be interpreted as executable code rather than being displayed as plain text.

Attack Vector

The attack is network-based and requires user interaction. An attacker can exploit this vulnerability by submitting a specially crafted research topic containing malicious JavaScript code. The attack flow proceeds as follows:

1. The attacker accesses the OrcaStatLLM-Researcher application and creates a research topic
2. The attacker includes a malicious JavaScript payload within the research topic input field
3. The application stores the unsanitized input in its database
4. When a victim user navigates to the Session Page and views the Log Messages, the stored malicious script executes in their browser
5. The attacker's JavaScript can then perform actions such as stealing session cookies, modifying page content, or redirecting users to malicious sites

The vulnerability does not require authentication to exploit, and no publicly available exploits have been identified. For detailed technical information, refer to the GitHub Security Advisory.

Detection Methods for CVE-2026-24903

Indicators of Compromise

• Presence of unexpected <script> tags, event handlers (e.g., onerror, onload), or encoded JavaScript within research topic fields in the database
• Unusual JavaScript execution errors logged in browser developer tools when viewing Session Page logs
• Reports from users experiencing unexpected redirects, pop-ups, or unusual behavior when viewing session logs
• Anomalous network requests originating from the Session Page to external domains

Detection Strategies

• Implement Web Application Firewall (WAF) rules to detect and block common XSS patterns in incoming requests
• Deploy Content Security Policy (CSP) headers to restrict script execution and report policy violations
• Monitor application logs for research topic submissions containing HTML/JavaScript syntax patterns
• Conduct periodic security scans of stored data for potential XSS payloads

Monitoring Recommendations

• Enable CSP reporting to capture and analyze attempted XSS attacks
• Set up alerts for database entries containing script tags or JavaScript event handlers
• Monitor for unusual session activity that may indicate successful XSS exploitation
• Review web server access logs for patterns consistent with XSS reconnaissance or exploitation attempts

How to Mitigate CVE-2026-24903

Immediate Actions Required

• Review and sanitize all existing research topic entries in the database to remove any stored XSS payloads
• Implement strict input validation on all user-supplied fields, particularly the research topic input
• Apply context-appropriate output encoding when rendering user-supplied content in the Session Page
• Deploy Content Security Policy (CSP) headers with strict script-src directives as a defense-in-depth measure

Patch Information

Consult the GitHub Security Advisory for the latest patch and remediation guidance from the OrcaStatLLM-Researcher maintainers. Update to the latest version of the application as soon as a security patch becomes available.

Workarounds

• Restrict access to the Session Page to trusted users only until a patch is applied
• Implement a Web Application Firewall (WAF) with XSS filtering rules in front of the application
• Apply input sanitization at the application layer to strip or encode HTML entities and JavaScript from research topic inputs
• Consider implementing a temporary read-only mode for log viewing that renders content as plain text

# Example: Add Content-Security-Policy header to web server configuration (Nginx)
# Add this to your server block or location directive
add_header Content-Security-Policy "default-src 'self'; script-src 'self'; object-src 'none'; frame-ancestors 'self';" always;