CVE-2024-38627 (GCVE-0-2024-38627)
Vulnerability from cvelistv5 – Published: 2024-06-21 10:18 – Updated: 2026-05-23 15:49
VLAI
EPSS
VEX
Title
stm class: Fix a double free in stm_register_device()
Summary
In the Linux kernel, the following vulnerability has been resolved:
stm class: Fix a double free in stm_register_device()
The put_device(&stm->dev) call will trigger stm_device_release() which
frees "stm" so the vfree(stm) on the next line is a double free.
Severity
No CVSS data available.
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-06-21 13:23 UTC
Assigner
References
9 references
Impacted products
2 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Linux | Linux |
Affected:
389b6699a2aa0b457aa69986e9ddf39f3b4030fd , < 6cc30ef8eb6d8f8d6df43152264bbf8835d99931
(git)
Affected: 389b6699a2aa0b457aa69986e9ddf39f3b4030fd , < a0450d3f38e7c6c0a7c0afd4182976ee15573695 (git) Affected: 389b6699a2aa0b457aa69986e9ddf39f3b4030fd , < 713fc00c571dde4af3db2dbd5d1b0eadc327817b (git) Affected: 389b6699a2aa0b457aa69986e9ddf39f3b4030fd , < 7419df1acffbcc90037f6b5a2823e81389659b36 (git) Affected: 389b6699a2aa0b457aa69986e9ddf39f3b4030fd , < 4bfd48bb6e62512b9c392c5002c11e1e3b18d247 (git) Affected: 389b6699a2aa0b457aa69986e9ddf39f3b4030fd , < 370c480410f60b90ba3e96abe73ead21ec827b20 (git) Affected: 389b6699a2aa0b457aa69986e9ddf39f3b4030fd , < d782a2db8f7ac49c33b9ca3e835500a28667d1be (git) Affected: 389b6699a2aa0b457aa69986e9ddf39f3b4030fd , < 3df463865ba42b8f88a590326f4c9ea17a1ce459 (git) Affected: b0351a51ffda593b2b1b35dd0c00a73505edb256 (git) Affected: 4.4.178 , < 4.5 (semver) |
guessed | |
| Linux | Linux |
Affected:
4.7
Unaffected: 0 , < 4.7 (semver) Unaffected: 4.19.316 , ≤ 4.19.* (semver) Unaffected: 5.4.278 , ≤ 5.4.* (semver) Unaffected: 5.10.219 , ≤ 5.10.* (semver) Unaffected: 5.15.161 , ≤ 5.15.* (semver) Unaffected: 6.1.93 , ≤ 6.1.* (semver) Unaffected: 6.6.33 , ≤ 6.6.* (semver) Unaffected: 6.9.4 , ≤ 6.9.* (semver) Unaffected: 6.10 , ≤ * (original_commit_for_fix) |
guessed |
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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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