CVE-2024-35940 (GCVE-0-2024-35940)
Vulnerability from cvelistv5 – Published: 2024-05-19 10:10 – Updated: 2026-05-12 11:53
VLAI
EPSS
VEX
Title
pstore/zone: Add a null pointer check to the psz_kmsg_read
Summary
In the Linux kernel, the following vulnerability has been resolved:
pstore/zone: Add a null pointer check to the psz_kmsg_read
kasprintf() returns a pointer to dynamically allocated memory
which can be NULL upon failure. Ensure the allocation was successful
by checking the pointer validity.
Severity
5.5 (Medium)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-05-20 15:01 UTC
CWE
- CWE-476 - NULL Pointer Dereference
Assigner
References
8 references
Impacted products
3 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Linux | Linux |
Affected:
d26c3321fe18dc74517dc1f518d584aa33b0a851 , < 98e2b97acb875d65bdfc75fc408e67975cef3041
(git)
Affected: d26c3321fe18dc74517dc1f518d584aa33b0a851 , < 0ff96ec22a84d80a18d7ae8ca7eb111c34ee33bb (git) Affected: d26c3321fe18dc74517dc1f518d584aa33b0a851 , < 635594cca59f9d7a8e96187600c34facb8bc0682 (git) Affected: d26c3321fe18dc74517dc1f518d584aa33b0a851 , < ec7256887d072f98c42cdbef4dcc80ddf84c7a70 (git) Affected: d26c3321fe18dc74517dc1f518d584aa33b0a851 , < 6f9f2e498eae7897ba5d3e33908917f68ff4abcc (git) Affected: d26c3321fe18dc74517dc1f518d584aa33b0a851 , < 98bc7e26e14fbb26a6abf97603d59532475e97f8 (git) |
guessed | |
| Linux | Linux |
Affected:
5.8
Unaffected: 0 , < 5.8 (semver) Unaffected: 5.10.215 , ≤ 5.10.* (semver) Unaffected: 5.15.155 , ≤ 5.15.* (semver) Unaffected: 6.1.86 , ≤ 6.1.* (semver) Unaffected: 6.6.27 , ≤ 6.6.* (semver) Unaffected: 6.8.6 , ≤ 6.8.* (semver) Unaffected: 6.9 , ≤ * (original_commit_for_fix) |
guessed | |
| Siemens | SIMATIC S7-1500 TM MFP - GNU/Linux subsystem |
Affected:
0 , < *
(custom)
|
guessed |
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"source": "Red Hat CSAF VEX",
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"url": "https://security.access.redhat.com/data/csaf/v2/vex/2024/cve-2024-35940.json",
"version": "3"
},
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"product_status:recommended": "670",
"source": "SUSE CSAF VEX",
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}
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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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