CVE-2026-49287
Raw vector
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:H/A:HSummary
CVE-2026-49287 is a high-severity Unsafe Reflection (CWE-470) vulnerability. Its CVSS base score is 7.4 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Reflective Code Loading (T1620); ranked at the 38th 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 SI-10 (Information Input Validation) and AC-3 (Access Enforcement) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-38057
Vulnerability Data
Statamic is a Laravel and Git powered content management system (CMS). Prior to 5.73.23 and 6.20.0, the fix for CVE-2026-41175 was incomplete. It addressed the issue in the query builder, but the same protection was not applied to in-memory collection…
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sorting. Manipulating sort parameters could result in the loss of content and assets. This requires a front-end template that passes request input into a tag's sort parameter. It is not exploitable by default — a template would need to be explicitly set up to sort by a visitor-controlled value. This has been fixed in 5.73.23 and 6.20.0.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Input validation directly stops externally supplied class or method names from selecting improper code via reflection.
Enforces authorization checks on the code or classes ultimately invoked, blocking unauthorized selections even if reflection is used.
Limits privileges of any code reached through unsafe reflection, reducing blast radius without stopping the selection itself.
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 avoid introducing externally controlled class selection via reflection.
Vulnerability identification processes can discover unsafe reflection during code review or scanning.
Preventing execution of unauthorized code can block exploitation of unsafe reflection at runtime.
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 standards directly forbid unsafe reflection and require whitelisting or static alternatives.
Security testing can detect and block unsafe reflection patterns before release.
Secure development lifecycle mandates input validation and design reviews that reduce unsafe reflection risks.
Application security requirements can explicitly prohibit or constrain reflection based on untrusted input.
Secure architecture principles discourage dynamic class loading from external data sources.
Access restrictions limit who can supply the malicious input but do not address the reflection flaw itself.