Vulnerability-Lookup

FKIE_CVE-2023-49610

Vulnerability from fkie_nvd - Published: 2024-02-01 23:15 - Updated: 2026-06-17 06:36

Severity

8.1 (High) - CVSS:3.1/AV:A/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:H
8.1 (High) - CVSS:3.1/AV:A/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:H

Summary

MachineSense FeverWarn Raspberry Pi-based devices lack input sanitization, which could allow an attacker on an adjacent network to send a message running commands or could overflow the stack.

References

URL	Tags
ics-cert@hq.dhs.gov	https://machinesense.com/pages/about-machinesense	Product
ics-cert@hq.dhs.gov	https://www.cisa.gov/news-events/ics-advisories/icsa-24-025-01	Third Party Advisory, US Government Resource
af854a3a-2127-422b-91ae-364da2661108	https://machinesense.com/pages/about-machinesense	Product
af854a3a-2127-422b-91ae-364da2661108	https://www.cisa.gov/news-events/ics-advisories/icsa-24-025-01	Third Party Advisory, US Government Resource

Impacted products

	Vendor	Product	Version
	machinesense	feverwarn_firmware	-
	machinesense	feverwarn	-

JSON

To clipboard

{
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          "product": "FeverWarn",
          "vendor": "MachineSense",
          "versions": [
            {
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              "version": "ESP32"
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            {
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              "version": "RaspberryPi"
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            {
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              "version": "DataHub RaspberryPi"
            }
          ]
        }
      ],
      "source": "ics-cert@hq.dhs.gov"
    }
  ],
  "configurations": [
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      "tags": [
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      "value": "\n\n\n\n\n\n\n\n\n\n\nMachineSense FeverWarn Raspberry Pi-based devices lack input sanitization, which could allow an attacker on an adjacent network to send a message running commands or could overflow the stack.\n\n\n\n\n\n\n\n\n\n\n\n"
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    }
  ],
  "id": "CVE-2023-49610",
  "lastModified": "2026-06-17T06:36:14.060",
  "metrics": {
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          "attackComplexity": "LOW",
          "attackVector": "ADJACENT_NETWORK",
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        "impactScore": 5.2,
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  "published": "2024-02-01T23:15:10.003",
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          "value": "NVD-CWE-noinfo"
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      ],
      "source": "nvd@nist.gov",
      "type": "Primary"
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  ]
}

CVE-2023-49610 (GCVE-0-2023-49610)

Vulnerability from cvelistv5 – Published: 2024-02-01 22:35 – Updated: 2024-10-17 16:03 Unsupported When Assigned

Title

MachineSense FeverWarn Improper Input Validation

Summary

MachineSense FeverWarn Raspberry Pi-based devices lack input sanitization, which could allow an attacker on an adjacent network to send a message running commands or could overflow the stack.

Severity

8.1 (High)


                        
                          CVSS:3.1/AV:A/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:H

SSVC

Exploitation: none Automatable: no Technical Impact: partial

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2024-10-17 16:02 UTC

CWE

CWE-20 - Improper Input Validation

Assigner

icscert icscert root

References

2 references

URL	Tags
https://www.cisa.gov/news-events/ics-advisories/i…
https://machinesense.com/pages/about-machinesense

Impacted products

1 product

Vendor	Product	Version
MachineSense	FeverWarn	Affected: ESP32 Affected: RaspberryPi Affected: DataHub RaspberryPi

Credits

Vera Mens of Claroty Research reported these vulnerabilities to CISA.

Show details on NVD website

JSON

To clipboard

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        "metrics": [
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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.

Detection rules are retrieved from Rulezet.

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.

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.