CVE-2026-13499 yashpokharna2555 CVE debrief

Who should care

Security teams and administrators responsible for the yashpokharna2555 restaurent-management-system should be aware of this vulnerability. Given the low CVSS score of 2.1, it may not be a high priority, but it still requires attention to prevent potential XSS attacks. Teams using this system should monitor for any updates or patches from the vendor and consider implementing compensating controls.

Technical summary

The CVE-2026-13499 vulnerability is a cross-site scripting (XSS) issue in the login_register.php file of the yashpokharna2555 restaurent-management-system. The vulnerability is caused by improper sanitization of user input in the Username argument. An attacker can exploit this vulnerability by injecting malicious JavaScript code, potentially leading to unauthorized actions or data theft. The CVSS:4.0 vector is AV:N/AC:L/AT:N/PR:N/UI:P/VC:N/VI:L/VA:N/SC:N/SI:N/SA:N/E:P/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X.

Defensive priority

Given the low CVSS severity of LOW and score of 2.1, this vulnerability is not considered high priority. However, security teams should still be aware of the issue and monitor for any updates or patches from the vendor.

Recommended defensive actions

• Review and monitor the system's codebase for similar vulnerabilities.
• Implement web application firewall (WAF) rules to detect and prevent XSS attacks.
• Ensure proper input validation and sanitization for user-supplied data.
• Consider implementing compensating controls, such as content security policy (CSP).
• Monitor for any updates or patches from the vendor and apply them as soon as they are available.

Evidence notes

The CVE-2026-13499 vulnerability was discovered in the yashpokharna2555 restaurent-management-system. The vulnerability is caused by improper sanitization of user input in the Username argument of the login_register.php file. The attack can be initiated remotely, and a public exploit has been released. The product uses a rolling release model, making specific version information for affected or updated releases unavailable.

Sources and references

Methodology and review provenance

AI-assisted synthesis based on stored public vulnerability evidence. System validation, approval state, and publication status do not by themselves establish human review of this revision. PatchSiren helps prioritize defensive review and does not prove exposure or remediation on any system.