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CVE Vulnerabilities

CVE-2023-52355

Out-of-bounds Write

Published: Jan 25, 2024 | Modified: Dec 10, 2025
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
NEGLIGIBLE
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Additional information
NVDhttps://nvd.nist.gov/vuln/detail/CVE-2023-52355
CWEhttps://cwe.mitre.org/data/definitions/787.html

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.

Weakness

The product writes data past the end, or before the beginning, of the intended buffer.

Affected Software

NameVendorStart VersionEnd Version
LibtiffLibtiff*4.6.0 (excluding)
Red Hat Enterprise Linux 9RedHatlibtiff-0:4.4.0-15.el9*
Red Hat AI Inference Server 3.2RedHatrhaiis/vllm-cuda-rhel9:sha256:ec961e5acfde5c1ad0a7e0e2c86a0bf56b9bc46357fa124f9db6dff1006076ab*
Red Hat AI Inference Server 3.2RedHatrhaiis/vllm-rocm-rhel9:sha256:7856bdb7ae0d643a7b9362c164d4d4fe3c0c7186f5fff73a7ae9835b3df52e57*
Red Hat AI Inference Server 3.2RedHatrhaiis/model-opt-cuda-rhel9:sha256:dce6b0ea03379bf06664a5200af8b5f5ae3fad13cdce6d21873843f22554800b*
Red Hat Discovery 2RedHatdiscovery/discovery-ui-rhel9:sha256:310df392f638ef6eca1a26db024ae2cb617db5932f886d2acddc92fb7289e740*
GdalUbuntuesm-apps/xenial*
GdalUbuntuesm-infra-legacy/trusty*
GdalUbuntutrusty*
GdalUbuntutrusty/esm*
GdalUbuntuxenial*
NeuronUbuntubionic*
NeuronUbuntuesm-apps/bionic*
NeuronUbuntuesm-apps/focal*
NeuronUbuntuesm-apps/jammy*
NeuronUbuntufocal*
NeuronUbuntujammy*
Qtwebengine-opensource-srcUbuntubionic*
Qtwebengine-opensource-srcUbuntudevel*
Qtwebengine-opensource-srcUbuntuesm-apps/bionic*
Qtwebengine-opensource-srcUbuntuesm-apps/focal*
Qtwebengine-opensource-srcUbuntuesm-apps/jammy*
Qtwebengine-opensource-srcUbuntufocal*
Qtwebengine-opensource-srcUbuntujammy*
Qtwebengine-opensource-srcUbuntumantic*
TexmakerUbuntubionic*
TexmakerUbuntudevel*
TexmakerUbuntuesm-apps/bionic*
TexmakerUbuntuesm-apps/focal*
TexmakerUbuntuesm-apps/jammy*
TexmakerUbuntufocal*
TexmakerUbuntujammy*
TexmakerUbuntumantic*
TiffUbuntubionic*
TiffUbuntudevel*
TiffUbuntuesm-infra-legacy/trusty*
TiffUbuntuesm-infra/bionic*
TiffUbuntuesm-infra/focal*
TiffUbuntuesm-infra/xenial*
TiffUbuntufocal*
TiffUbuntujammy*
TiffUbuntumantic*
TiffUbuntutrusty*
TiffUbuntutrusty/esm*
TiffUbuntuxenial*

Potential Mitigations

  • Use a language that does not allow this weakness to occur or provides constructs that make this weakness easier to avoid.

  • For example, many languages that perform their own memory management, such as Java and Perl, are not subject to buffer overflows. Other languages, such as Ada and C#, typically provide overflow protection, but the protection can be disabled by the programmer.

  • Be wary that a language’s interface to native code may still be subject to overflows, even if the language itself is theoretically safe.

  • Use a vetted library or framework that does not allow this weakness to occur or provides constructs that make this weakness easier to avoid.

  • Examples include the Safe C String Library (SafeStr) by Messier and Viega [REF-57], and the Strsafe.h library from Microsoft [REF-56]. These libraries provide safer versions of overflow-prone string-handling functions.

  • 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.

  • Consider adhering to the following rules when allocating and managing an application’s memory:

  • 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].

  • Use a CPU and operating system that offers Data Execution Protection (using hardware NX or XD bits) or the equivalent techniques that simulate this feature in software, such as PaX [REF-60] [REF-61]. These techniques ensure that any instruction executed is exclusively at a memory address that is part of the code segment.

  • For more information on these techniques see D3-PSEP (Process Segment Execution Prevention) from D3FEND [REF-1336].

References

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