Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:HSummary
CVE-2026-60646 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Oracle Webcenter Content. Its CVSS base score is 8.0 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 7th 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 IA-12 (Identity Proofing) and IA-5 (Authenticator Management) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-47005
Vulnerability Data
Vulnerability in the Oracle WebCenter Content product of Oracle Fusion Middleware (component: Web Content Management). Supported versions that are affected are 12.2.1.4.0 and 14.1.2.0.0. Easily exploitable vulnerability allows low privileged attacker with network access via HTTP to compromise Oracle WebCenter…
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Content. Successful attacks require human interaction from a person other than the attacker. Successful attacks of this vulnerability can result in takeover of Oracle WebCenter Content. CVSS 3.1 Base Score 8.0 (Confidentiality, Integrity and Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:U/C:H/I:H/A:H).
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V3.3.2V3.5.1V10.2.1V3.7.2
Mitigating Controls (NIST 800-53 r5) AI
Identity proofing at appropriate assurance levels ensures forgotten-password recovery cannot succeed without strong verification of the user.
Authenticator management requires secure distribution, reset, and verification procedures that directly address weak password recovery flows.
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.
Access enforcement requires verifying that state-changing requests originate from the authenticated user rather than a forged cross-site source.
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.
Credential lifecycle management directly includes password reset/recovery flows.
Identity proofing is the core control that prevents weak or bypassed recovery mechanisms.
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).
Authentication policy covers strength/MFA but does not address recovery path weaknesses.
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.
Strong authentication-information lifecycle rules directly address weak password-recovery flows.
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.
By denying access to phishing or malicious sites, the control lowers the likelihood that a user will be tricked into submitting a forged request that performs an unintended action on another site.
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.