Raw vector
CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:U/C:H/I:N/A:NSummary
CVE-2026-9073 is a medium-severity Insertion of Sensitive Information into Log File (CWE-532) vulnerability in Redhat Satellite. Its CVSS base score is 6.2 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Credentials In Files (T1552.001); ranked at the 5th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as AI Agent Protocols and Integrations; in the Protocol-Specific Risks risk domain.
The strongest mitigations our analysis identified map to AU-3 (Content of Audit Records) and CM-6 (Configuration Settings) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-38603
Vulnerability Data
A flaw was found in foreman-mcp-server. This component utilizes two distinct logging mechanisms that can expose sensitive session and authentication data. One mechanism logs session identifiers, which are treated as authentication credentials, at an informational level. The other, when debug…
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logging is enabled, incompletely sanitizes HTTP request headers, leading to the cleartext logging of sensitive information such as authorization tokens and API keys. This vulnerability can result in a confidentiality breach, as sensitive authentication data is persisted in plain text within container logs, increasing the risk if logs are forwarded to a centralized platform.
- CWE(s)
AI Security AnalysisAI
- AI Category
- AI Agent Protocols and Integrations
- Risk Domain
- Protocol-Specific Risks
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: mcp
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
CWE-532 directly results in credentials persisted in log files (T1552.001).
Likely ATT&CK TechniquesAI
Techniques this vulnerability likely enables, inferred from its description, weakness type, and attributed-actor tradecraft. Confidence is per-technique.
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
Mitigating Controls (NIST 800-53 r5) AI
Directly specifies audit record content so that sensitive session IDs, authorization tokens, and API keys are excluded from logs.
Requires error and debug handling routines to avoid writing unsanitized sensitive header or credential data into logs.
Enforces secure logging configuration settings that disable verbose/debug levels or enforce header sanitization to prevent exposure of authentication material.
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 prohibit writing sensitive data to logs; eliminating the weakness satisfies only one narrow slice of the control.
Log generation configuration can and should exclude sensitive data, but the control statement focuses on availability rather than content filtering.
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.
Requiring de-identification and privacy controls before logs leave the organization reduces the chance that sensitive data inadvertently captured in logs becomes exposed to external parties.
By defining what records must be kept, where, and for how long, the control discourages the inadvertent inclusion of sensitive information in logs or other externally accessible files that fall outside the formal record system.
Mandating deletion of temporary files and logs that may contain sensitive information prevents those artifacts from remaining accessible after the data is no longer needed.
When log entries are produced from masked data sets, the control prevents the inadvertent insertion of sensitive values into externally accessible log files.
DLP inspection of logs and file transfers can detect and block the inadvertent placement of sensitive tokens or credentials into externally accessible log files before they are written or transmitted.
Requiring restrictions on free-text fields and proper error-message handling stops developers from embedding or leaking sensitive data into logs or diagnostic output.