CVE-2026-112896 in Pillow
Summary
by MITRE • 10/20/2026
Pillow is a Python imaging library. Prior to 13.0.0, Pillow's TIFF encoder can read beyond a ColorMap buffer when retained TIFF metadata has a plane size inconsistent with BitsPerSample. A small TIFF can be opened normally and then retain a crafted ColorMap that is reused when the image is saved. An application that re-encodes the attacker's TIFF with libtiff can trigger the out-of-bounds read. This can disclose heap memory in the output or crash the process. This issue is fixed in version 13.0.0.
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Analysis
by VulDB Data Team • 10/20/2026
The vulnerability identified in Pillow, a widely used Python imaging library, represents a critical security flaw within its TIFF encoder component that affects versions prior to 13.0.0. The core technical deficiency lies in the handling of retained metadata during the encoding process, specifically concerning the ColorMap buffer associated with Tagged Image File Format specifications. When processing certain TIFF files, the encoder fails to adequately validate the consistency between the plane size and the BitsPerSample attribute found within the image's metadata headers. This lack of rigorous boundary checking allows for a scenario where an attacker can craft a maliciously constructed TIFF file that appears valid upon initial opening but contains embedded metadata designed to exploit this logical error during subsequent write operations.
The operational mechanism of this vulnerability relies on a two-stage attack vector involving both reading and writing phases. Initially, the application opens the crafted TIFF image without issue because the decoder is more lenient or correctly handles the input structure at that stage. However, when the user attempts to save or re-encode this image using Pillow's encoder, which may interface with underlying libraries such as libtiff, the system reads beyond the allocated boundaries of the ColorMap buffer. This out-of-bounds read occurs because the encoder trusts the retained metadata plane size without verifying it against the actual BitsPerSample configuration, leading to memory access violations that extend into adjacent heap regions.
The impact of this vulnerability is significant from both a confidentiality and availability perspective. The primary consequence is the potential disclosure of sensitive information residing in the process's heap memory. By reading beyond the intended buffer limits, an attacker can extract arbitrary data stored in nearby memory locations, which may include cryptographic keys, session tokens, or other proprietary application states. Furthermore, depending on the specific state of the heap and the operating system's memory protection mechanisms, this out-of-bounds access can lead to process crashes, resulting in a denial of service for applications that rely on Pillow for image processing tasks. This dual nature makes it particularly dangerous in server-side environments where availability is as critical as data integrity.
From a classification standpoint, this vulnerability aligns with CWE-125, which describes Out-of-bounds Read vulnerabilities, indicating an improper limitation upon the range of values to which a pointer can point or be indexed. In terms of offensive security frameworks such as MITRE ATT&CK, this behavior is consistent with techniques related to Memory Scraping or Data from Information Repositories, where attackers leverage memory access errors to exfiltrate data without direct file system interaction. The exploitation path suggests that any application allowing user-uploaded TIFF files to be processed and re-saved by Pillow is potentially vulnerable if it does not implement additional input validation layers before invoking the encoder.
To mitigate this risk, organizations utilizing Pillow must immediately upgrade to version 13.0.0 or later, where the developers have implemented proper bounds checking for the ColorMap buffer relative to BitsPerSample values. For environments that cannot update instantly due to dependency constraints, implementing strict input validation on uploaded TIFF files is recommended. This includes verifying metadata consistency before processing and limiting the types of images accepted by the application. Additionally, deploying runtime protection mechanisms such as Address Sanitizers during development or using memory-safe wrappers can help detect similar issues in other parts of the codebase that may not yet be patched. Regular security audits focusing on image parsing libraries are essential to prevent exploitation of these low-level memory management errors.