GSD-2022-24294
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
A regular expression used in Apache MXNet (incubating) is vulnerable to a potential denial-of-service by excessive resource consumption. The bug could be exploited when loading a model in Apache MXNet that has a specially crafted operator name that would cause the regular expression evaluation to use excessive resources to attempt a match. This issue affects Apache MXNet versions prior to 1.9.1.
Aliases
Aliases
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"description": "A regular expression used in Apache MXNet (incubating) is vulnerable to a potential denial-of-service by excessive resource consumption. The bug could be exploited when loading a model in Apache MXNet that has a specially crafted operator name that would cause the regular expression evaluation to use excessive resources to attempt a match. This issue affects Apache MXNet versions prior to 1.9.1.",
"id": "GSD-2022-24294"
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"value": "Apache MXNet would like to thank Dwi Siswanto for reporting this issue."
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"description": "A regular expression used in Apache MXNet (incubating) is vulnerable to a potential denial-of-service by excessive resource consumption. The bug could be exploited when loading a model in Apache MXNet that has a specially crafted operator name that would cause the regular expression evaluation to use excessive resources to attempt a match. This issue affects Apache MXNet versions prior to 1.9.1.",
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"date": "2023-03-01",
"description": "A regular expression used in Apache MXNet (incubating) is vulnerable to a potential denial-of-service by excessive resource consumption. The bug could be exploited when loading a model in Apache MXNet that has a specially crafted operator name that would cause the regular expression evaluation to use excessive resources to attempt a match. This issue affects Apache MXNet versions prior to 1.9.1.",
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"lastModifiedDate": "2022-08-01T16:01Z",
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}
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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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