Raw vector
CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:C/C:H/I:H/A:HSummary
CVE-2026-5483 is a high-severity Insertion of Sensitive Information Into Sent Data (CWE-201) vulnerability in Redhat Openshift Ai. Its CVSS base score is 8.5 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Network Sniffing (T1040); ranked at the 40th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Enterprise AI Assistants; in the Privacy and Disclosure risk domain.
The strongest mitigations our analysis identified map to AC-3 (Access Enforcement) and AC-4 (Information Flow Enforcement) — 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-2026-5483, published on 2026-04-10, is a vulnerability in the odh-dashboard component of Red Hat OpenShift AI (RHOAI). The flaw enables the disclosure of Kubernetes Service Account tokens through a NodeJS endpoint, which could allow an attacker to gain unauthorized access to Kubernetes resources. It has been classified under CWE-201 (Exposure of Sensitive Information to an Unauthorized Actor) with a CVSS v3.1 base score of 8.5 (AV:N/AC:H/PR:L/UI:N/S:C/C:H/I:H/A:H).
An attacker with low privileges (PR:L) can exploit this vulnerability over the network (AV:N), though it requires high attack complexity (AC:H) and no user interaction (UI:N). Successful exploitation changes scope (S:C) and results in high impacts to confidentiality, integrity, and availability (C:H/I:H/A:H), permitting the use of disclosed service account tokens to access Kubernetes resources without authorization.
Red Hat has released patches addressing this issue in security advisories RHSA-2026:7397, RHSA-2026:7398, RHSA-2026:7403, and RHSA-2026:7404, with additional details available on the CVE page at https://access.redhat.com/security/cve/CVE-2026-5483. Security practitioners should review these errata for mitigation and update instructions.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-21547
Vulnerability Data
A flaw was found in odh-dashboard in Red Hat Openshift AI. This vulnerability in the `odh-dashboard` component of Red Hat OpenShift AI (RHOAI) allows for the disclosure of Kubernetes Service Account tokens through a NodeJS endpoint. This could enable an…
more
attacker to gain unauthorized access to Kubernetes resources.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Enterprise AI Assistants
- Risk Domain
- Privacy and Disclosure
- 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
—
—
- 1 hardening rule · 1 OS baseline
V14.2.3
Mitigating Controls (NIST 800-53 r5) AI
Directly enforces policy-based information flow rules that block transmission of sensitive data to unauthorized actors.
Enforces authorizations on logical access so that sensitive data is not released to unauthorized recipients.
Requires validation of outbound information to ensure sensitive content is not disclosed in responses or messages.
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 prevent insertion of sensitive data into application outputs and messages.
Monitoring runtime data flows and outputs can detect sensitive data being transmitted.
Protecting data-in-transit can include filtering or encrypting to avoid exposing sensitive content.
Protecting data-in-use includes removing confidential values before they are processed or sent.
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.
Data-masking techniques can prevent sensitive values from appearing in transmitted payloads.
Classification identifies sensitive data so it is not inadvertently transmitted.
Labelling makes sensitive data visible to developers and prevents accidental inclusion in outbound messages.
Information-transfer rules directly govern what data may be sent to external parties.
PII-protection requirements reduce the chance of sending personal data to unauthorized recipients.
DLP controls inspect and block outbound flows that contain sensitive information.