CVE-2021-22890 (GCVE-0-2021-22890)
Vulnerability from cvelistv5 – Published: 2021-04-01 17:46 – Updated: 2025-06-09 15:11
VLAI
EPSS
VEX
Summary
curl 7.63.0 to and including 7.75.0 includes vulnerability that allows a malicious HTTPS proxy to MITM a connection due to bad handling of TLS 1.3 session tickets. When using a HTTPS proxy and TLS 1.3, libcurl can confuse session tickets arriving from the HTTPS proxy but work as if they arrived from the remote server and then wrongly "short-cut" the host handshake. When confusing the tickets, a HTTPS proxy can trick libcurl to use the wrong session ticket resume for the host and thereby circumvent the server TLS certificate check and make a MITM attack to be possible to perform unnoticed. Note that such a malicious HTTPS proxy needs to provide a certificate that curl will accept for the MITMed server for an attack to work - unless curl has been told to ignore the server certificate check.
Severity
4.3 (Medium)
SSVC
Exploitation: poc
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2025-06-09 15:11 UTC
CWE
- CWE-300 - Man-in-the-Middle (CWE-300)
Assigner
References
9 references
| URL | Tags |
|---|---|
| https://hackerone.com/reports/1129529 | x_refsource_MISC |
| https://curl.se/docs/CVE-2021-22890.html | x_refsource_MISC |
| https://lists.fedoraproject.org/archives/list/pac… | vendor-advisoryx_refsource_FEDORA |
| https://lists.fedoraproject.org/archives/list/pac… | vendor-advisoryx_refsource_FEDORA |
| https://lists.fedoraproject.org/archives/list/pac… | vendor-advisoryx_refsource_FEDORA |
| https://security.gentoo.org/glsa/202105-36 | vendor-advisoryx_refsource_GENTOO |
| https://www.oracle.com//security-alerts/cpujul2021.html | x_refsource_MISC |
| https://security.netapp.com/advisory/ntap-2021052… | x_refsource_CONFIRM |
| https://cert-portal.siemens.com/productcert/pdf/s… | x_refsource_CONFIRM |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| n/a | https://github.com/curl/curl |
Affected:
7.63.0 to and including 7.75.0
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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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