GSD-2023-22578
Vulnerability from gsd - Updated: 2023-12-13 01:20Details
Due to improper artibute filtering in the sequalize js library, can a attacker peform SQL injections.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2023-22578",
"id": "GSD-2023-22578"
},
"gsd": {
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"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
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"details": "Due to improper artibute filtering in the sequalize js library, can a attacker peform SQL injections.",
"id": "GSD-2023-22578",
"modified": "2023-12-13T01:20:43.370283Z",
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"credits": [
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"lang": "en",
"value": "Thomas Rinsma and Kevin Valk (Codean)"
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"package_slug": "npm/@sequelize/core",
"pubdate": "2023-02-16",
"solution": "Upgrade to version 7.0.0-alpha.20 or above. *Note*: 7.0.0-alpha.20 may be an unstable version. Use caution.",
"title": "Sequelize vulnerable to Improper Filtering of Special Elements",
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"package_slug": "npm/sequelize",
"pubdate": "2023-02-16",
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"title": "False Positive",
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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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