SUSE-SU-2024:1074-1
Vulnerability from csaf_suse - Published: 2024-03-30 00:04 - Updated: 2026-09-20 15:43Summary
Security update for qpid-proton
Severity
Important
Notes
Title of the patch: Security update for qpid-proton
Description of the patch: This update for qpid-proton fixes the following issues:
- CVE-2019-0223: Fixed TLS Man in the Middle Vulnerability (bsc#1133158).
The following non-security bugs were fixed:
- Fix build with OpenSSL 3.0.0 (bsc#1172267)
- Sort linked .o files to make package build reproducible (bsc#1041090)
- Fix build with gcc8 (bsc#1084627)
- Move libqpid-proton-core to a different package to fix a rpmlint
error (bsc#1191783)
Patchnames: SUSE-2024-1074,SUSE-SLE-Module-Packagehub-Subpackages-15-SP5-2024-1074,openSUSE-SLE-15.5-2024-1074
Terms of use: CSAF 2.0 data is provided by SUSE under the Creative Commons License 4.0 with Attribution (CC-BY-4.0).
7.4 (High)
Affected products
Recommended
9 products
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: openSUSE Leap 15.5:python3-python-qpid-proton-0:0.38.0-150000.6.3.1.aarch64 | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 15.5:python3-python-qpid-proton-0:0.38.0-150000.6.3.1.ppc64le | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 15.5:python3-python-qpid-proton-0:0.38.0-150000.6.3.1.s390x | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 15.5:python3-python-qpid-proton-0:0.38.0-150000.6.3.1.x86_64 | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 15.5:qpid-proton-devel-0:0.38.0-150000.6.3.1.aarch64 | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 15.5:qpid-proton-devel-0:0.38.0-150000.6.3.1.ppc64le | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 15.5:qpid-proton-devel-0:0.38.0-150000.6.3.1.s390x | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 15.5:qpid-proton-devel-0:0.38.0-150000.6.3.1.x86_64 | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 15.5:qpid-proton-devel-doc-0:0.38.0-150000.6.3.1.noarch | — |
Vendor Fix
|
Threats
Impact
important
References
12 references
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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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