OPENSUSE-SU-2020:0022-1
Vulnerability from csaf_opensuse - Published: 2020-01-13 15:18 - Updated: 2020-01-13 15:18Summary
Security update for libgcrypt
Severity
Moderate
Notes
Title of the patch: Security update for libgcrypt
Description of the patch: This update for libgcrypt fixes the following issues:
Security issues fixed:
- CVE-2019-13627: Mitigation against an ECDSA timing attack (bsc#1148987).
Bug fixes:
- Added CMAC AES self test (bsc#1155339).
- Added CMAC TDES self test missing (bsc#1155338).
- Fix test dsa-rfc6979 in FIPS mode.
This update was imported from the SUSE:SLE-15-SP1:Update update project.
Patchnames: openSUSE-2020-22
Terms of use: CSAF 2.0 data is provided by SUSE under the Creative Commons License 4.0 with Attribution (CC-BY-4.0).
5.5 (Medium)
Affected products
Recommended
11 products
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: openSUSE Leap 15.1:libgcrypt-cavs-1.8.2-lp151.9.7.1.i586 | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 15.1:libgcrypt-cavs-1.8.2-lp151.9.7.1.x86_64 | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 15.1:libgcrypt-devel-1.8.2-lp151.9.7.1.i586 | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 15.1:libgcrypt-devel-1.8.2-lp151.9.7.1.x86_64 | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 15.1:libgcrypt-devel-32bit-1.8.2-lp151.9.7.1.x86_64 | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 15.1:libgcrypt20-1.8.2-lp151.9.7.1.i586 | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 15.1:libgcrypt20-1.8.2-lp151.9.7.1.x86_64 | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 15.1:libgcrypt20-32bit-1.8.2-lp151.9.7.1.x86_64 | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 15.1:libgcrypt20-hmac-1.8.2-lp151.9.7.1.i586 | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 15.1:libgcrypt20-hmac-1.8.2-lp151.9.7.1.x86_64 | — |
Vendor Fix
|
|
| Unresolved product id: openSUSE Leap 15.1:libgcrypt20-hmac-32bit-1.8.2-lp151.9.7.1.x86_64 | — |
Vendor Fix
|
Threats
Impact
moderate
References
10 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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