VAR-201804-0587
Vulnerability from variot - Updated: 2023-12-18 12:01An exploitable Code Execution vulnerability exists in the RequestForPatientInfoEEGfile functionality of Natus Xltek NeuroWorks 8. A specially crafted network packet can cause a stack buffer overflow resulting in arbitrary command execution. An attacker can send a malicious packet to trigger this vulnerability. Natus Xltek NeuroWorks Contains a buffer error vulnerability.Information is obtained, information is altered, and service operation is disrupted (DoS) There is a possibility of being put into a state. Natus Xltek NeuroWorks is a universal software platform for EEG testing, long-term monitoring, ICU monitoring and sleep research at Natus Medical. Natus Xltek NeuroWorks/SleepWorks are prone to the following security vulnerabilities: 1. Multiple stack-based buffer-overflow vulnerabilities 2. Failed attacks will cause denial of service conditions. Xltek NeuroWorks/SleepWorks 8 are vulnerable
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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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