CVE-2023-33176 (GCVE-0-2023-33176)
Vulnerability from cvelistv5 – Published: 2023-06-26 19:50 – Updated: 2024-11-12 15:18
VLAI
EPSS
VEX
Title
Blind SSRF When Uploading Presentation in BigBlueButton
Summary
BigBlueButton is an open source virtual classroom designed to help teachers teach and learners learn. In affected versions are affected by a Server-Side Request Forgery (SSRF) vulnerability. In an `insertDocument` API request the user is able to supply a URL from which the presentation should be downloaded. This URL was being used without having been successfully validated first. An update to the `followRedirect` method in the `PresentationUrlDownloadService` has been made to validate all URLs to be used for presentation download. Two new properties `presentationDownloadSupportedProtocols` and `presentationDownloadBlockedHosts` have also been added to `bigbluebutton.properties` to allow administrators to define what protocols a URL must use and to explicitly define hosts that a presentation cannot be downloaded from. All URLs passed to `insertDocument` must conform to the requirements of the two previously mentioned properties. Additionally, these URLs must resolve to valid addresses, and these addresses must not be local or loopback addresses. There are no workarounds. Users are advised to upgrade to a patched version of BigBlueButton.
Severity
4.8 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-11-12 15:18 UTC
CWE
- CWE-918 - Server-Side Request Forgery (SSRF)
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://github.com/bigbluebutton/bigbluebutton/se… | x_refsource_CONFIRM |
| https://github.com/bigbluebutton/bigbluebutton/pu… | x_refsource_MISC |
| https://github.com/bigbluebutton/bigbluebutton/pu… | x_refsource_MISC |
| https://github.com/bigbluebutton/bigbluebutton/co… | x_refsource_MISC |
| https://github.com/bigbluebutton/bigbluebutton/co… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| bigbluebutton | bigbluebutton |
Affected:
>= 2.6.0, < 2.6.9
Affected: < 2.5.18 |
guessed |
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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.
Vulnerability timeline
Sightings and recorded dates, grouped by day · UTC
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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.