CVE-2021-38544 (GCVE-0-2021-38544)
Vulnerability from cvelistv5 – Published: 2021-08-11 15:25 – Updated: 2024-08-04 01:44
VLAI
EPSS
VEX
Summary
Sony SRS-XB33 and SRS-XB43 devices through 2021-08-09 allow remote attackers to recover speech signals from an LED on the device, via a telescope and an electro-optical sensor, aka a "Glowworm" attack. The power indicator LED of the speakers is connected directly to the power line, as a result, the intensity of a device's power indicator LED is correlative to the power consumption. The sound played by the speakers affects their power consumption and as a result is also correlative to the light intensity of the LEDs. By analyzing measurements obtained from an electro-optical sensor directed at the power indicator LEDs of the speakers, we can recover the sound played by them.
Severity
5.9 (Medium)
CWE
- n/a
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://www.nassiben.com/glowworm-attack | x_refsource_MISC |
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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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