CVE-2026-112891 in Pillow
Summary
by MITRE • 10/20/2026
Pillow is a Python imaging library. From 9.2.0 until 13.0.0, Pillow performs quadratic copying when saving a GIF with a long comment. _get_global_header() repeatedly copies portions of comment data while assembling the global header, including comments retained from an untrusted input image. An attacker can supply an image with a large GIF comment to an application that saves it as GIF. This can consume excessive CPU and delay or deny service. This issue is fixed in version 13.0.0.
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Analysis
by VulDB Data Team • 10/20/2026
The vulnerability identified within the Pillow Python imaging library, spanning versions from 9.2.0 through 13.0.0, represents a significant efficiency flaw rooted in algorithmic inefficiency rather than traditional memory corruption or code injection techniques. Specifically, this issue manifests as an inefficient quadratic copying operation during the process of saving GIF files that contain long comment fields. The root cause lies within the internal function _get_global_header(), which is responsible for assembling the global header block required by the Graphics Interchange Format specification. When processing images with substantial metadata in their comments section, this function repeatedly copies portions of the comment data rather than utilizing more efficient string concatenation or memory allocation strategies that would result in linear time complexity relative to the input size.
From a technical perspective, the quadratic behavior implies that as the length of the GIF comment increases, the computational effort required to save the file grows exponentially. This occurs because each iteration of the header assembly process likely involves creating new string objects and copying existing data into them multiple times, leading to an O(n^2) time complexity where n is the size of the comment. In practical terms, a moderately sized malicious GIF with a large embedded text block can trigger disproportionate CPU usage on the server or application processing it. This behavior does not result in arbitrary code execution but rather serves as a vector for resource exhaustion attacks.
The operational impact of this vulnerability centers primarily on Denial of Service conditions. An attacker who can supply an untrusted image to any application that utilizes Pillow for saving GIFs with comments can deliberately trigger this inefficient processing path. By submitting images with increasingly large comment sections, the attacker forces the target system to consume excessive CPU cycles and memory resources during file serialization. This resource consumption can lead to significant delays in legitimate operations or complete service denial if the server's computational limits are reached before the task completes. Such scenarios are particularly dangerous for web applications that accept user-uploaded images and subsequently process them, as a single malicious upload could degrade performance for all users sharing the same resources.
This vulnerability aligns with CWE-400, which classifies issues related to Uncontrolled Resource Consumption, specifically highlighting how inefficient algorithms can lead to resource exhaustion under specific input conditions. Furthermore, in the context of the MITRE ATT&CK framework, this behavior is indicative of techniques used for Denial of Service attacks where adversaries leverage application logic flaws or algorithmic inefficiencies rather than network-level flooding to disrupt service availability. The attack vector requires direct interaction with the image processing component, typically via file upload mechanisms or API endpoints that accept image data and perform save operations on GIF formats containing comments.
Mitigation strategies for this vulnerability involve immediate upgrading of the Pillow library to version 13.0.0 or later, where the quadratic copying issue has been resolved through algorithmic improvements in the header assembly process. For organizations unable to upgrade immediately due to dependency constraints, implementing input validation and size limits on GIF comment fields can serve as a temporary compensating control. By restricting the maximum length of metadata accepted during image processing, applications can prevent attackers from supplying payloads large enough to trigger significant performance degradation. Additionally, deploying rate limiting or resource quotas for file upload operations can help mitigate the impact if such inputs are processed before patching is applied.