Raw vector
CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:C/C:H/I:H/A:NSummary
CVE-2026-34940 is a high-severity OS Command Injection (CWE-78) vulnerability in Kubeai Kubeai. Its CVSS base score is 8.7 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Command and Scripting Interpreter (T1059); ranked at the 37th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as NLP and Transformers; in the Supply Chain and Deployment risk domain.
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-2026-34940 is a command injection vulnerability (CWE-78) affecting KubeAI, an AI inference operator for Kubernetes, in versions prior to 0.23.2. The issue resides in the ollamaStartupProbeScript() function within internal/modelcontroller/engine_ollama.go, which constructs a shell command string using fmt.Sprintf with unsanitized model URL components (ref and modelParam). This command is then executed via bash -c as part of a Kubernetes startup probe for model server pods.
An attacker with privileges to create or update Model custom resources can exploit this vulnerability over the network with low complexity and no user interaction required. Successful exploitation allows injection of arbitrary shell commands that execute with the privileges of the model server pods, potentially leading to high confidentiality and integrity impacts across the scoped cluster, as indicated by the CVSS score of 8.7 (AV:N/AC:L/PR:H/UI:N/S:C/C:H/I:H/A:N).
The vulnerability is fixed in KubeAI version 0.23.2. Security practitioners should upgrade to this version or later, as detailed in the GitHub Security Advisory at https://github.com/kubeai-project/kubeai/security/advisories/GHSA-324q-cwx9-7crr.
This vulnerability is particularly relevant to AI/ML workloads, as KubeAI manages inference operations on Kubernetes clusters, highlighting risks in operator-managed AI deployments. No public information on real-world exploitation is available.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-19355
Vulnerability Data
KubeAI is an AI inference operator for kubernetes. Prior to 0.23.2, the ollamaStartupProbeScript() function in internal/modelcontroller/engine_ollama.go constructs a shell command string using fmt.Sprintf with unsanitized model URL components (ref, modelParam). This shell command is executed via bash -c as a…
more
Kubernetes startup probe. An attacker who can create or update Model custom resources can inject arbitrary shell commands that execute inside model server pods. This vulnerability is fixed in 0.23.2.
- CWE(s)
AI Security AnalysisAI
- AI Category
- NLP and Transformers
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.2.5V1.2.8V15.2.5
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover missing or incorrect command sanitization during development.
Input validation directly neutralizes or rejects special characters that would otherwise alter OS command structure.
Least privilege reduces the permissions available to any process that could be subverted by injected commands.
Least functionality restricts available OS commands and interpreters, limiting the blast radius of injection.
Secure engineering principles require proper neutralization of untrusted input before command construction.
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 require secure coding and input handling that blocks command-injection defects, yet the single broad outcome leaves many specific neutralization vectors and verification gaps unaddressed.
Routine patching/maintenance can remediate known command-injection CVEs in dependencies (partial forward) but does nothing to stop developers from introducing improper neutralization in custom code (none reverse).
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 and code review target insecure use of operating-system command interfaces, catching command-injection flaws introduced during development.