Raw vector
CVSS:3.1/AV:N/AC:L/PR:H/UI:R/S:C/C:H/I:H/A:NSummary
CVE-2026-21361 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Adobe Commerce. Its CVSS base score is 8.1 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 37th 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-2026-21361 is a stored Cross-Site Scripting (XSS) vulnerability (CWE-79) affecting Adobe Commerce versions 2.4.9-alpha3, 2.4.8-p3, 2.4.7-p8, 2.4.6-p13, 2.4.5-p15, 2.4.4-p16, and earlier. Published on 2026-03-11, it carries a CVSS v3.1 base score of 8.1 (AV:N/AC:L/PR:H/UI:R/S:C/C:H/I:H/A:N). The flaw enables a high-privileged attacker to inject malicious scripts into vulnerable form fields.
A high-privileged attacker (PR:H) can exploit this vulnerability over the network (AV:N) with low complexity (AC:L) by injecting malicious JavaScript into affected form fields. When a victim browses to the page containing the injected script, it executes in their browser (UI:R), potentially enabling session takeover and resulting in high confidentiality and integrity impacts (C:H/I:H) due to the changed scope (S:C).
The Adobe security advisory provides details on mitigation; see https://helpx.adobe.com/security/products/magento/apsb26-05.html.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-11071
Vulnerability Data
Adobe Commerce versions 2.4.9-alpha3, 2.4.8-p3, 2.4.7-p8, 2.4.6-p13, 2.4.5-p15, 2.4.4-p16 and earlier are affected by a stored Cross-Site Scripting (XSS) vvulnerability that could be abused by a high-privileged attacker to inject malicious scripts into vulnerable form fields. Malicious JavaScript may be…
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executed in a victim’s browser when they browse to the page containing the vulnerable field. A successful attacker can abuse this to achieve session takeover, increasing the confidentiality, and integrity impact to high. Exploitation of this issue requires user interaction in that a victim must browse to the page containing the vulnerable field.
- 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.