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 |
|---|---|---|---|
| Pillow | Python | 11.2.1 (including) | 11.2.1 (including) |
| Red Hat Enterprise Linux AI 1.5 | RedHat | rhelai1/instructlab-nvidia-rhel9:1.5.3-1756791365 | * |
| Red Hat Enterprise Linux AI 1.5 | RedHat | rhelai1/bootc-intel-rhel9:1.5.3-1756724193 | * |
| Red Hat Enterprise Linux AI 1.5 | RedHat | rhelai1/bootc-gcp-nvidia-rhel9:1.5.3-1756815294 | * |
| Red Hat Enterprise Linux AI 1.5 | RedHat | rhelai1/bootc-aws-nvidia-rhel9:1.5.3-1756815228 | * |
| Red Hat Enterprise Linux AI 1.5 | RedHat | rhelai1/bootc-amd-rhel9:1.5.3-1756800437 | * |
| Red Hat Enterprise Linux AI 1.5 | RedHat | rhelai1/bootc-azure-amd-rhel9:1.5.3-1756815221 | * |
| Red Hat Enterprise Linux AI 1.5 | RedHat | rhelai1/bootc-nvidia-rhel9:1.5.3-1756799326 | * |
| Red Hat Enterprise Linux AI 1.5 | RedHat | rhelai1/bootc-azure-nvidia-rhel9:1.5.3-1756815370 | * |
| Red Hat Enterprise Linux AI 1.5 | RedHat | rhelai1/instructlab-amd-rhel9:1.5.3-1756791391 | * |
| Red Hat Enterprise Linux AI 1.5 | RedHat | rhelai1/instructlab-intel-rhel9:1.5.3-1757955810 | * |
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].