CVE Vulnerabilities

CVE-2025-48379

Heap-based Buffer Overflow

Published: Jul 01, 2025 | Modified: Jul 03, 2025
CVSS 3.x
N/A
Source:
NVD
CVSS 2.x
RedHat/V2
RedHat/V3
7.1 IMPORTANT
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:H
Ubuntu
MEDIUM

Pillow is a Python imaging library. In versions 11.2.0 to before 11.3.0, there is a heap buffer overflow when writing a sufficiently large (>64k encoded with default settings) image in the DDS format due to writing into a buffer without checking for available space. This only affects users who save untrusted data as a compressed DDS image. This issue has been patched in version 11.3.0.

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

Name Vendor Start Version End Version
Red Hat Enterprise Linux AI 1.5.3 RedHat registry.redhat.io/rhelai1/instructlab-nvidia-rhel9:sha256:f4e7a03db9b24711381b5e1279e19eadf6b7dd20510711a17212261fb67f3e11 *
Red Hat Enterprise Linux AI 1.5.3 RedHat registry.redhat.io/rhelai1/bootc-gcp-nvidia-rhel9:sha256:e0f422a906d386596295e99b64c6158ae44b6b8a12be30868865e76742fccb17 *
Red Hat Enterprise Linux AI 1.5.3 RedHat registry.redhat.io/rhelai1/bootc-aws-nvidia-rhel9:sha256:a169a0d43b63280b9f43b99e6f9910cf0f404c7a9089d30dc06a0aa7fe747b8b *
Red Hat Enterprise Linux AI 1.5.3 RedHat registry.redhat.io/rhelai1/bootc-nvidia-rhel9:sha256:4a40fcdfb64b4cec6dfb0d0ee5c475fc89124ce80d911dd85f5951238b6c980c *
Red Hat Enterprise Linux AI 1.5.3 RedHat registry.redhat.io/rhelai1/instructlab-amd-rhel9:sha256:f34417c39c2f3b78f306d4249e892a9edf61f2a88bb18a3484c1df9716bdd324 *

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