CVE-2020-16969 (GCVE-0-2020-16969)
Vulnerability from cvelistv5 – Published: 2020-10-16 22:18 – Updated: 2024-08-04 13:45
VLAI
EPSS
VEX
Title
Microsoft Exchange Information Disclosure Vulnerability
Summary
<p>An information disclosure vulnerability exists in how Microsoft Exchange validates tokens when handling certain messages. An attacker who successfully exploited the vulnerability could use this to gain further information from a user.</p>
<p>To exploit the vulnerability, an attacker could include specially crafted OWA messages that could be loaded, without warning or filtering, from the attacker-controlled URL. This callback vector provides an information disclosure tactic used in web beacons and other types of tracking systems.</p>
<p>The security update corrects the way that Exchange handles these token validations.</p>
Severity
CWE
- Information Disclosure
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://portal.msrc.microsoft.com/en-US/security-… | x_refsource_MISC |
Impacted products
5 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Microsoft | Microsoft Exchange Server 2019 Cumulative Update 6 |
Affected:
15.02.0 , < publication
(custom)
cpe:2.3:a:microsoft:exchange_server:2019:cumulative_update_6:*:*:*:*:*:* |
||
| Microsoft | Microsoft Exchange Server 2016 Cumulative Update 17 |
Affected:
15.01.0 , < publication
(custom)
cpe:2.3:a:microsoft:exchange_server:2016:cumulative_update_17:*:*:*:*:*:* |
||
| Microsoft | Microsoft Exchange Server 2019 Cumulative Update 7 |
Affected:
15.02.0 , < publication
(custom)
cpe:2.3:a:microsoft:exchange_server:2019:cumulative_update_7:*:*:*:*:*:* |
||
| Microsoft | Microsoft Exchange Server 2016 Cumulative Update 18 |
Affected:
15.01.0 , < publication
(custom)
cpe:2.3:a:microsoft:exchange_server:2016:cumulative_update_18:*:*:*:*:*:* |
||
| Microsoft | Microsoft Exchange Server 2013 Cumulative Update 23 |
Affected:
15.00.0 , < publication
(custom)
cpe:2.3:a:microsoft:exchange_server:2013:cumulative_update_23:*:*:*:*:*:* |
Date Public
2020-10-13 07:00
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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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