Vulnerability-Lookup

VAR-202106-1587

Vulnerability from variot - Updated: 2025-02-14 22:46

An improper input validation vulnerability in sdfffd_parse_chunk_PROP() in libsdffextractor library prior to SMR MAY-2021 Release 1 allows attackers to execute arbitrary code on mediaextractor process. Android Contains a classic buffer overflow vulnerability.Information is obtained, information is tampered with, and service is disrupted (DoS) It may be put into a state. Samsung libsdffextractor library is a component of Samsung mobile devices.

Samsung libsdffextractor library has an input validation error vulnerability

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CVE-2021-25385 (GCVE-0-2021-25385)

Vulnerability from cvelistv5 – Published: 2021-06-11 14:45 – Updated: 2024-08-03 20:03

Summary

Severity

9 (Critical)


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

CWE

CWE-121 - Stack-based Buffer Overflow

Assigner

Samsung Mobile

References

1 reference

URL	Tags
https://security.samsungmobile.com/securityUpdate…	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version
Samsung Mobile	Samsung Mobile Devices	Affected: O(8.1), P(9.x), Q(10.0), R(11.0) , < SMR MAY-2021 Release 1 (custom)

Show details on NVD website

JSON

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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.