CVE-2026-63337
Raw vector
CVSS:4.0/AV:N/AC:L/AT:P/PR:L/UI:P/VC:H/VI:H/VA:H/SC:L/SI:L/SA:L/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:XCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2026-63337 is a high-severity Unsafe Reflection (CWE-470) vulnerability. Its CVSS base score is 7.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Reflective Code Loading (T1620); ranked at the 24th 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-2026-61043
Vulnerability Data
The RabbitMQ Java client library allows Java and JVM-based applications to connect to and interact with RabbitMQ nodes. Prior to 5.33.0, com.rabbitmq.tools.jsonrpc.ProcedureDescription receives a javaReturnType value in an untrusted system.describe response and passes it through JSONUtil.tryFill, setJavaReturnType, and computeReturnTypeAsJavaClass to…
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Class.forName(javaReturnType) with initialization enabled. An attacker able to answer the JsonRpcClient request through a shared broker or network interception can select a class already present in the victim JVM and trigger its static initializer, while JsonRpcClient.java later passes getReturnType output to mapper.parse and may also create type confusion. Successful exploitation can affect confidentiality, integrity, and availability in the client process. This issue is fixed in version 5.33.0.
- 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.