CVE-2024-31463 (GCVE-0-2024-31463)
Vulnerability from cvelistv5 – Published: 2024-04-17 15:21 – Updated: 2024-08-02 01:52
VLAI
EPSS
VEX
Title
Ironic-image allows unauthenticated local access to Ironic API
Summary
Ironic-image is an OpenStack Ironic deployment packaged and configured by Metal3. When the reverse proxy mode is enabled by the `IRONIC_REVERSE_PROXY_SETUP` variable set to `true`, 1) HTTP basic credentials are validated on the HTTPD side in a separate container, not in the Ironic service itself and 2) Ironic listens in host network on a private port 6388 on localhost by default. As a result, when the reverse proxy mode is used, any Pod or local Unix user on the control plane Node can access the Ironic API on the private port without authentication. A similar problem affects Ironic Inspector (`INSPECTOR_REVERSE_PROXY_SETUP` set to `true`), although the attack potential is smaller there. This issue affects operators deploying ironic-image in the reverse proxy mode, which is the recommended mode when TLS is used (also recommended), with the `IRONIC_PRIVATE_PORT` variable unset or set to a numeric value. In this case, an attacker with enough privileges to launch a pod on the control plane with host networking can access Ironic API and use it to modify bare-metal machine, e.g. provision them with a new image or change their BIOS settings. This vulnerability is fixed in 24.1.1.
Severity
4.7 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-07-03 15:16 UTC
CWE
- CWE-288 - Authentication Bypass Using an Alternate Path or Channel
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/metal3-io/ironic-image/securit… | x_refsource_CONFIRM |
| https://github.com/metal3-io/ironic-image/pull/494 | x_refsource_MISC |
| https://github.com/metal3-io/ironic-image/commit/… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| metal3-io | ironic-image |
Affected:
< 24.1.1
|
guessed |
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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