Raw vector
CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:H/I:H/A:HCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2024-32030 is a high-severity Code Injection (CWE-94) vulnerability in Github (inferred from references). Its CVSS base score is 8.1 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Command and Scripting Interpreter (T1059); ranked in the top 2% of CVEs by exploit likelihood; 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.
Kafka UI, an open-source web interface for managing Apache Kafka clusters, contains a deserialization vulnerability in its optional JMX-based broker monitoring feature. The component accepts RMI connections to user-specified broker addresses and ports; because JMX relies on the RMI protocol, an attacker-supplied endpoint can return malicious serialized objects that are deserialized by the Kafka UI backend when the dynamic.config.enabled setting is present or when the attacker already controls a monitored Kafka cluster.
An unauthenticated remote attacker can therefore supply a malicious RMI listener instead of a legitimate JMX port, triggering gadget chains present in the application classpath and achieving remote code execution on the Kafka UI server. The same vector can be used by an attacker who already has access to a legitimate cluster to expand control to the management UI itself. The issue is exacerbated by the fact that Kafka UI ships without authentication enabled by default.
The vulnerability is tracked as GHSL-2023-230 and was fixed in release 0.7.2; the project repository contains the corresponding patch commits and pull request that disable unsafe JMX handling. No workarounds are documented. The associated EPSS score stands at 0.8172 with no material post-disclosure increase from a lower baseline.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-29867
Vulnerability Data
Kafka UI is an Open-Source Web UI for Apache Kafka Management. Kafka UI API allows users to connect to different Kafka brokers by specifying their network address and port. As a separate feature, it also provides the ability to monitor…
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the performance of Kafka brokers by connecting to their JMX ports. JMX is based on the RMI protocol, so it is inherently susceptible to deserialization attacks. A potential attacker can exploit this feature by connecting Kafka UI backend to its own malicious broker. This vulnerability affects the deployments where one of the following occurs: 1. dynamic.config.enabled property is set in settings. It's not enabled by default, but it's suggested to be enabled in many tutorials for Kafka UI, including its own README.md. OR 2. an attacker has access to the Kafka cluster that is being connected to Kafka UI. In this scenario the attacker can exploit this vulnerability to expand their access and execute code on Kafka UI as well. Instead of setting up a legitimate JMX port, an attacker can create an RMI listener that returns a malicious serialized object for any RMI call. In the worst case it could lead to remote code execution as Kafka UI has the required gadget chains in its classpath. This issue may lead to post-auth remote code execution. This is particularly dangerous as Kafka-UI does not have authentication enabled by default. This issue has been addressed in version 0.7.2. All users are advised to upgrade. There are no known workarounds for this vulnerability. These issues were discovered and reported by the GitHub Security lab and is also tracked as GHSL-2023-230.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.3.1
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation finds code paths that accept and execute externally influenced strings.
Input validation directly stops untrusted data from being used to construct executable code without neutralization.
Least privilege limits the damage an injected code fragment can perform once executed.
Requiring documented secure development standards and tools enforces use of safe code-generation APIs and escaping.
Engineering principles such as safe deserialization and input sanitization structurally prevent the weakness from being introduced.
Integrity verification tools can detect malformed or tampered serialized data after the fact.
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.
PR.PS-06's SDLC practices directly target injection flaws via secure coding and testing (mostly), yet as a single broad outcome it leaves many code-generation specifics unaddressed (partial).
PR.DS-10 protects runtime data confidentiality/integrity but has no bearing on neutralizing externally influenced input during code generation, so neither direction shows any preventive effect.
PR.PS-02 addresses only post-deployment updates/patching and cannot prevent introduction of unsafe deserialization code, yet it can remediate some instances when the flaw exists in outdated libraries or components.
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.
Security testing includes validation of deserialization routines and the use of untrusted data, reducing the likelihood that unsafe object reconstruction will be deployed.
Banning unapproved code samples and unauthenticated web services, combined with secure-coding standards and SAST, prevents the dynamic generation or inclusion of attacker-supplied code.
Regular scanning of third-party libraries and timely patching reduce the likelihood that unsafe deserialization vulnerabilities remain active.