CVE-2019-11090 (GCVE-0-2019-11090)
Vulnerability from cvelistv5 – Published: 2019-12-18 21:08 – Updated: 2024-08-04 22:40
VLAI
EPSS
VEX
Summary
Cryptographic timing conditions in the subsystem for Intel(R) PTT before versions 11.8.70, 11.11.70, 11.22.70, 12.0.45, 13.0.0 and 14.0.10; Intel(R) TXE 3.1.70 and 4.0.20; Intel(R) SPS before versions SPS_E5_04.01.04.305.0, SPS_SoC-X_04.00.04.108.0, SPS_SoC-A_04.00.04.191.0, SPS_E3_04.01.04.086.0, SPS_E3_04.08.04.047.0 may allow an unauthenticated user to potentially enable information disclosure via network access.
Severity
5.9 (Medium)
CWE
- Information Disclosure
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://www.intel.com/content/www/us/en/security-… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| n/a | Intel(R) PTT |
Affected:
See provided reference
|
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"id": "CVE-2019-11090",
"lastModified": "2024-11-21T04:20:31.127",
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"published": "2019-12-18T22:15:12.237",
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Advisory\"]},{\"url\":\"https://www.intel.com/content/www/us/en/security-center/advisory/intel-sa-00241.html\",\"source\":\"af854a3a-2127-422b-91ae-364da2661108\",\"tags\":[\"Vendor Advisory\"]}]}}",
"redhat_vex": {
"aggregate_severity": "Moderate",
"current_release_date": "2025-11-21T16:52:47+00:00",
"cve": "CVE-2019-11090",
"id": "CVE-2019-11090",
"initial_release_date": "2019-01-01T00:00:00+00:00",
"product_status:known_not_affected": "42",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "hw: ECDSA signature timing vulnerabilities in TPM module",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2019/cve-2019-11090.json",
"version": "3"
},
"suse_vex": {
"aggregate_severity": "moderate",
"current_release_date": "2025-02-17T07:41:47Z",
"cve": "CVE-2019-11090",
"id": "CVE-2019-11090",
"initial_release_date": "2023-02-15T04:12:58Z",
"source": "SUSE CSAF VEX",
"status": "interim",
"title": "SUSE CVE CVE-2019-11090",
"url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2019-11090.json",
"version": "5"
}
}
}
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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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