SUSE-SU-2019:0243-2
Vulnerability from csaf_suse - Published: 2019-04-27 15:38 - Updated: 2026-09-20 13:04Summary
Security update for python3
Severity
Important
Notes
Title of the patch: Security update for python3
Description of the patch: This update for python3 fixes the following issues:
Security issue fixed:
- CVE-2019-5010: Fixed a denial-of-service vulnerability in the X509 certificate parser (bsc#1122191)
- CVE-2018-20406: Fixed a integer overflow via a large LONG_BINPUT (bsc#1120644)
Patchnames: SUSE-2019-243,SUSE-SLE-SAP-12-SP1-2019-243
Terms of use: CSAF 2.0 data is provided by SUSE under the Creative Commons License 4.0 with Attribution (CC-BY-4.0).
Affected products
Recommended
3 products
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: SUSE Linux Enterprise Server for SAP Applications 12 SP1:libpython3_4m1_0-0:3.4.6-25.21.1.x86_64 | — |
Vendor Fix
|
|
| Unresolved product id: SUSE Linux Enterprise Server for SAP Applications 12 SP1:python3-0:3.4.6-25.21.1.x86_64 | — |
Vendor Fix
|
|
| Unresolved product id: SUSE Linux Enterprise Server for SAP Applications 12 SP1:python3-base-0:3.4.6-25.21.1.x86_64 | — |
Vendor Fix
|
Threats
Impact
moderate
7.5 (High)
Affected products
Recommended
3 products, the same list as for
CVE-2018-20406
Threats
Impact
moderate
References
13 references
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"text": "Modules/_pickle.c in Python before 3.7.1 has an integer overflow via a large LONG_BINPUT value that is mishandled during a \"resize to twice the size\" attempt. This issue might cause memory exhaustion, but is only relevant if the pickle format is used for serializing tens or hundreds of gigabytes of data. This issue is fixed in: v3.4.10, v3.4.10rc1; v3.5.10, v3.5.10rc1, v3.5.7, v3.5.7rc1, v3.5.8, v3.5.8rc1, v3.5.8rc2, v3.5.9; v3.6.10, v3.6.10rc1, v3.6.11, v3.6.11rc1, v3.6.12, v3.6.7, v3.6.7rc1, v3.6.7rc2, v3.6.8, v3.6.8rc1, v3.6.9, v3.6.9rc1; v3.7.1, v3.7.1rc1, v3.7.1rc2, v3.7.2, v3.7.2rc1, v3.7.3, v3.7.3rc1, v3.7.4, v3.7.4rc1, v3.7.4rc2, v3.7.5, v3.7.5rc1, v3.7.6, v3.7.6rc1, v3.7.7, v3.7.7rc1, v3.7.8, v3.7.8rc1, v3.7.9.",
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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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