Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:NSummary
CVE-2026-4107 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Zohocorp Manageengine Exchange Reporter Plus. Its CVSS base score is 7.3 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 41th 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-2026-4107 is a stored cross-site scripting (XSS) vulnerability, classified under CWE-79, affecting Zohocorp ManageEngine Exchange Reporter Plus versions prior to 5802. The issue resides specifically in the Folder Message Count and Size report, where malicious scripts can be persistently stored. Published on 2026-04-03, it carries a CVSS v3.1 base score of 7.3 (AV:N/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:N), reflecting high potential impacts on confidentiality and integrity over a network with low attack complexity.
An attacker with low privileges (PR:L), such as an authenticated user, can exploit this vulnerability remotely (AV:N) by injecting malicious payloads into the affected report. Exploitation requires user interaction (UI:R), typically when a higher-privileged administrator views the tampered report, triggering script execution in the victim's browser context within the unchanged security scope (S:U). Successful attacks enable high confidentiality breaches, such as session hijacking or data exfiltration, and integrity violations like unauthorized modifications.
The official ManageEngine advisory at https://www.manageengine.com/products/exchange-reports/advisory/CVE-2026-4107.html details mitigation, recommending an upgrade to version 5802 or later, which addresses the vulnerability.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-18625
Vulnerability Data
Zohocorp ManageEngine Exchange Reporter Plus versions before 5802 are vulnerable to Stored XSS in Folder Message Count and Size report.
- 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.