CVE-2023-52355 (GCVE-0-2023-52355)
Vulnerability from cvelistv5 – Published: 2024-01-25 20:03 – Updated: 2026-08-03 14:42
VLAI
EPSS
VEX
Title
Libtiff: tiffrasterscanlinesize64 produce too-big size and could cause oom
Summary
An out-of-memory flaw was found in libtiff that could be triggered by passing a crafted tiff file to the TIFFRasterScanlineSize64() API. This flaw allows a remote attacker to cause a denial of service via a crafted input with a size smaller than 379 KB.
Severity
7.5 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-02-21 18:49 UTC
CWE
- CWE-787 - Out-of-bounds Write
Assigner
References
13 references
Impacted products
14 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
|
Affected:
0 , < 4.6.0
(semver)
|
||||
| Red Hat | Red Hat Enterprise Linux 10 |
Unaffected:
0:4.6.0-8.el10_2.4 , < *
(rpm)
cpe:/o:redhat:enterprise_linux:10.2 |
||
| Red Hat | Red Hat Enterprise Linux 10.0 Extended Update Support |
Unaffected:
0:4.6.0-6.el10_0.4 , < *
(rpm)
cpe:/o:redhat:enterprise_linux_eus:10.0 |
||
| Red Hat | Red Hat Enterprise Linux 9 |
Unaffected:
0:4.4.0-15.el9 , < *
(rpm)
cpe:/a:redhat:enterprise_linux:9::appstream cpe:/a:redhat:enterprise_linux:9::crb |
||
| Red Hat | Red Hat Enterprise Linux 9.6 Extended Update Support |
Unaffected:
0:4.4.0-13.el9_6.5 , < *
(rpm)
cpe:/a:redhat:rhel_eus:9.6::appstream cpe:/a:redhat:rhel_eus:9.6::crb |
||
| Red Hat | Red Hat AI Inference Server 3.2 |
Unaffected:
3.2.2-1765379088 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.2::el9 |
||
| Red Hat | Red Hat AI Inference Server 3.2 |
Unaffected:
3.2.2-1765379049 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.2::el9 |
||
| Red Hat | Red Hat AI Inference Server 3.2 |
Unaffected:
3.2.2-1764871796 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.2::el9 |
||
| Red Hat | Red Hat AI Inference Server 3.2 |
Unaffected:
1772160593 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.2::el9 |
||
| Red Hat | Red Hat AI Inference Server 3.2 |
Unaffected:
1772160625 , < *
(rpm)
cpe:/a:redhat:ai_inference_server:3.2::el9 |
||
| Red Hat | Red Hat Discovery 2 |
Unaffected:
2.4.0-1763656152 , < *
(rpm)
cpe:/a:redhat:discovery:2::el9 |
||
| Red Hat | Red Hat Enterprise Linux 6 |
cpe:/o:redhat:enterprise_linux:6
|
||
| Red Hat | Red Hat Enterprise Linux 7 |
cpe:/o:redhat:enterprise_linux:7
|
||
| Red Hat | Red Hat Enterprise Linux 8 |
cpe:/o:redhat:enterprise_linux:8
|
Date Public
2023-11-03 00:00
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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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