Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:HSummary
CVE-2026-28405 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Markusproject Markus. Its CVSS base score is 8.0 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 13th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
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-2026-28405 is a cross-site scripting vulnerability (CWE-79) affecting MarkUs, an open-source web application used for the submission and grading of student assignments. In versions prior to 2.9.1, the route `/courses/<:course_id>/assignments/<:assignment_id>/submissions/html_content` reads the contents of a student-submitted file and renders it without proper sanitization, enabling the injection and execution of malicious scripts. The vulnerability was published on 2026-03-05 and carries a CVSS v3.1 base score of 8.0 (AV:N/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:H).
An authenticated user with low privileges, such as a student, can exploit this vulnerability by uploading a file containing malicious HTML or JavaScript payload. Exploitation requires a victim—typically an instructor or another authorized user—to access the affected route for that submission, triggering the unsanitized rendering and script execution in the victim's browser context. Successful attacks can result in high-impact compromise of confidentiality, integrity, and availability, such as session hijacking, data theft, or further system manipulation.
The issue has been addressed in MarkUs version 2.9.1, where sanitization was added to prevent script injection. Administrators should upgrade to this patched release immediately. Additional details on the fix are provided in the GitHub security advisory (GHSA-p5pc-pxrj-3893), release notes for v2.9.1, and the specific patching commit (55d74f2ddb72d2ec2f29aa2b4cb6b2da10755036).
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-9870
Vulnerability Data
MarkUs is a web application for the submission and grading of student assignments. Prior to version 2.9.1, the courses/<:course_id>/assignments/<:assignment_id>/submissions/html_content route reads the contents of a student-submitted file and renders them without sanitization. This issue has been patched in version 2.9.1.
- 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.