Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:H/A:NSummary
CVE-2025-63611 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Phpgurukul Hostel Management System. Its CVSS base score is 8.7 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 18th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2025-63611 is a stored cross-site scripting (XSS) vulnerability, mapped to CWE-79, affecting phpgurukul Hostel Management System version 2.1. The flaw occurs in user-provided complaint fields, specifically the "Explain the Complaint" input submitted via /register-complaint.php. These values are stored without escaping and rendered directly in the admin interface at /admin/complaint-details.php?cid=<id>, enabling injected HTML or JavaScript to execute in an administrator's browser when viewing the complaint details.
The vulnerability can be exploited by a low-privileged user (PR:L) over the network (AV:N) with low complexity (AC:L), though it requires administrator interaction (UI:R) to view the malicious complaint. Upon execution, the attack changes scope (S:C), granting high confidentiality and integrity impacts (C:H/I:H) with no availability disruption (A:N), as reflected in its CVSS v3.1 base score of 8.7. This allows attackers to run arbitrary scripts in the admin's session context, potentially leading to session hijacking or further compromise.
Advisories and references, including a detailed analysis on Medium and the official project page at phpgurukul.com/hostel-management-system/, provide further context on the issue, published on 2026-01-08T16:15:45.057. Practitioners should consult these for any recommended patches or workarounds specific to the software.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-1516
Vulnerability Data
Cross-Site Scripting in phpgurukul Hostel Management System v2.1 user-provided complaint fields (Explain the Complaint) submitted via /register-complaint.php are stored and rendered unescaped in the admin viewer (/admin/complaint-details.php?cid=<id>). When an administrator opens the complaint, injected HTML/JavaScript executes in the admin's browser.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.1.2V1.3.2
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover missing or incorrect input neutralization through targeted web-application tests.
Input validation directly enforces neutralization of untrusted data before it reaches web output generation.
Output filtering can catch or sanitize unneutralized script content before it is served to users.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure SDLC practices directly target introduction of XSS via coding standards/testing (mostly), yet the single broad outcome leaves many specific neutralization vectors unaddressed (partial).
Patching and EOL replacement can remediate known XSS instances in libraries or frameworks (partial) but do nothing to enforce input neutralization in application code (none).
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Secure-coding testing and automated code-analysis tools are applied to detect improper neutralization of script-related content during web-page generation.
Knowledge exchange on emerging attack techniques and patches reduces the likelihood that cross-site scripting flaws remain unaddressed in deployed applications.
Operational indicators of compromise for web-application attacks can be incorporated into WAF or input-filtering rules, lowering the likelihood that unsanitized data reaches the browser.
Requiring language-specific secure-coding standards and automated scanning during the SDLC catches missing output encoding or improper neutralization of untrusted data before the software reaches production.
Secure-coding standards, SAST scans and removal of insecure code samples together eliminate the failure to neutralize script content that produces cross-site scripting flaws.
Webpage malware scanning and block-listing of known malicious sites reduce the likelihood that reflected or stored script payloads reach a user’s browser.