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CVE Vulnerabilities

CVE-2023-52356

Heap-based Buffer Overflow

Published: Jan 25, 2024 | Modified: Jun 10, 2026
CVSS 3.x
7.5
HIGH
Source:
NVD
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
CVSS 2.x
RedHat/V2
RedHat/V3
7.5 MODERATE
CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H
Ubuntu
MEDIUM
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Additional information
NVDhttps://nvd.nist.gov/vuln/detail/CVE-2023-52356
CWEhttps://cwe.mitre.org/data/definitions/122.html

A segment fault (SEGV) flaw was found in libtiff that could be triggered by passing a crafted tiff file to the TIFFReadRGBATileExt() API. This flaw allows a remote attacker to cause a heap-buffer overflow, leading to a denial of service.

Weakness

A heap overflow condition is a buffer overflow, where the buffer that can be overwritten is allocated in the heap portion of memory, generally meaning that the buffer was allocated using a routine such as malloc().

Affected Software

NameVendorStart VersionEnd Version
LibtiffLibtiff- (including)- (including)
Red Hat Enterprise Linux 10RedHatlibtiff-0:4.6.0-6.el10_1.2*
Red Hat Enterprise Linux 10.0 Extended Update SupportRedHatlibtiff-0:4.6.0-6.el10_0.2*
Red Hat Enterprise Linux 8RedHatlibtiff-0:4.0.9-32.el8_10*
Red Hat Enterprise Linux 9RedHatlibtiff-0:4.4.0-15.el9*
Red Hat Enterprise Linux 9.6 Extended Update SupportRedHatlibtiff-0:4.4.0-13.el9_6.3*
Red Hat AI Inference Server 3.2RedHatrhaiis/vllm-cuda-rhel9:3.2.2-1765379088*
Red Hat AI Inference Server 3.2RedHatrhaiis/vllm-rocm-rhel9:3.2.2-1765379049*
Red Hat AI Inference Server 3.2RedHatrhaiis/model-opt-cuda-rhel9:3.2.2-1764871796*
Red Hat AI Inference Server 3.2RedHatrhaiis/model-opt-cuda-rhel9:1780681984*
Red Hat AI Inference Server 3.2RedHatrhaiis/vllm-cuda-rhel9:1772160593*
Red Hat AI Inference Server 3.2RedHatrhaiis/vllm-rocm-rhel9:1772160625*
Red Hat AI Inference Server 3.2RedHatrhaiis/vllm-cuda-rhel9:1775740563*
Red Hat AI Inference Server 3.3RedHatrhaiis/vllm-spyre-rhel9:1778244546*
Red Hat AI Inference Server 3.3RedHatrhaiis/vllm-cuda-rhel9:1775680192*
Red Hat AI Inference Server 3.3RedHatrhaiis/vllm-rocm-rhel9:1775680262*
Red Hat AI Inference Server 3.3RedHatrhaiis/model-opt-cuda-rhel9:1775749857*
Red Hat Discovery 2RedHatdiscovery/discovery-ui-rhel9:2.4.0-1763656152*
GdalUbuntuesm-apps/xenial*
GdalUbuntutrusty*
GdalUbuntutrusty/esm*
GdalUbuntuxenial*
Qtwebengine-opensource-srcUbuntubionic*
Qtwebengine-opensource-srcUbuntufocal*
Qtwebengine-opensource-srcUbuntumantic*
Qtwebengine-opensource-srcUbuntuoracular*
Qtwebengine-opensource-srcUbuntuplucky*
TexmakerUbuntubionic*
TexmakerUbuntufocal*
TexmakerUbuntumantic*
TexmakerUbuntuoracular*
TexmakerUbuntuplucky*
TiffUbuntubionic*
TiffUbuntudevel*
TiffUbuntuesm-infra-legacy/trusty*
TiffUbuntuesm-infra-legacy/xenial*
TiffUbuntuesm-infra/bionic*
TiffUbuntuesm-infra/focal*
TiffUbuntuesm-infra/xenial*
TiffUbuntufocal*
TiffUbuntujammy*
TiffUbuntumantic*
TiffUbuntunoble*
TiffUbuntuoracular*
TiffUbuntuplucky*
TiffUbuntuquesting*
TiffUbunturesolute*
TiffUbuntutrusty*
TiffUbuntutrusty/esm*
TiffUbuntuxenial*

Potential Mitigations

  • Use automatic buffer overflow detection mechanisms that are offered by certain compilers or compiler extensions. Examples include: the Microsoft Visual Studio /GS flag, Fedora/Red Hat FORTIFY_SOURCE GCC flag, StackGuard, and ProPolice, which provide various mechanisms including canary-based detection and range/index checking.
  • D3-SFCV (Stack Frame Canary Validation) from D3FEND [REF-1334] discusses canary-based detection in detail.
  • Run or compile the software using features or extensions that randomly arrange the positions of a program’s executable and libraries in memory. Because this makes the addresses unpredictable, it can prevent an attacker from reliably jumping to exploitable code.
  • Examples include Address Space Layout Randomization (ASLR) [REF-58] [REF-60] and Position-Independent Executables (PIE) [REF-64]. Imported modules may be similarly realigned if their default memory addresses conflict with other modules, in a process known as “rebasing” (for Windows) and “prelinking” (for Linux) [REF-1332] using randomly generated addresses. ASLR for libraries cannot be used in conjunction with prelink since it would require relocating the libraries at run-time, defeating the whole purpose of prelinking.
  • For more information on these techniques see D3-SAOR (Segment Address Offset Randomization) from D3FEND [REF-1335].

References

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