CVE-2021-23404 (GCVE-0-2021-23404)
Vulnerability from cvelistv5 – Published: 2021-09-08 10:50 – Updated: 2024-09-16 18:29
VLAI
EPSS
VEX
Title
Cross-site Request Forgery (CSRF)
Summary
This affects all versions of package sqlite-web. The SQL dashboard area allows sensitive actions to be performed without validating that the request originated from the application. This could enable an attacker to trick a user into performing these actions unknowingly through a Cross Site Request Forgery (CSRF) attack.
Severity
CWE
- Cross-site Request Forgery (CSRF)
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://snyk.io/vuln/SNYK-PYTHON-SQLITEWEB-1316324 | x_refsource_MISC |
| https://github.com/coleifer/sqlite-web/blob/2e7c8… | x_refsource_MISC |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| n/a | sqlite-web |
Affected:
0 , < unspecified
(custom)
|
Date Public
2021-09-08 00:00
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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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