CVE-2024-45008 (GCVE-0-2024-45008)
Vulnerability from cvelistv5 – Published: 2024-09-04 19:54 – Updated: 2026-05-11 20:33
VLAI
EPSS
VEX
Title
Input: MT - limit max slots
Summary
In the Linux kernel, the following vulnerability has been resolved:
Input: MT - limit max slots
syzbot is reporting too large allocation at input_mt_init_slots(), for
num_slots is supplied from userspace using ioctl(UI_DEV_CREATE).
Since nobody knows possible max slots, this patch chose 1024.
Severity
No CVSS data available.
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-09-04 20:17 UTC
Assigner
References
10 references
Impacted products
2 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Linux | Linux |
Affected:
38e7afe96c7c0ad900824911c61fdb04078033dc , < 2829c80614890624456337e47320289112785f3e
(git)
Affected: 38e7afe96c7c0ad900824911c61fdb04078033dc , < 87f610a1a7fbdb1f2e3d90b54c955bd3b8a0c322 (git) Affected: 38e7afe96c7c0ad900824911c61fdb04078033dc , < 05dd9aabd04f9b5eb04dab9bb83d8c3e982d7549 (git) Affected: 38e7afe96c7c0ad900824911c61fdb04078033dc , < 95f73d01f547dfc67fda3022c51e377a0454b505 (git) Affected: 38e7afe96c7c0ad900824911c61fdb04078033dc , < 94736334b8a25e4fae8daa6934e54a31f099be43 (git) Affected: 38e7afe96c7c0ad900824911c61fdb04078033dc , < 8f04edd554d191834e9e1349ef030318ea6b11ba (git) Affected: 38e7afe96c7c0ad900824911c61fdb04078033dc , < cd19f1799c32ba7b874474b1b968815ce5364f73 (git) Affected: 38e7afe96c7c0ad900824911c61fdb04078033dc , < 99d3bf5f7377d42f8be60a6b9cb60fb0be34dceb (git) |
guessed | |
| Linux | Linux |
Affected:
2.6.36
Unaffected: 0 , < 2.6.36 (semver) Unaffected: 4.19.321 , ≤ 4.19.* (semver) Unaffected: 5.4.283 , ≤ 5.4.* (semver) Unaffected: 5.10.225 , ≤ 5.10.* (semver) Unaffected: 5.15.166 , ≤ 5.15.* (semver) Unaffected: 6.1.107 , ≤ 6.1.* (semver) Unaffected: 6.6.48 , ≤ 6.6.* (semver) Unaffected: 6.10.7 , ≤ 6.10.* (semver) Unaffected: 6.11 , ≤ * (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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