CVE-2026-112401 in Pillow
Summary
by MITRE • 10/20/2026
Pillow is a Python imaging library. From 5.4.0 until 13.0.0, Pillow performs quadratic copying while parsing GIF comments. GifImageFile._seek() repeatedly appends immutable bytes for comment sub-blocks and multiple comment extensions, and frame zero is parsed during Image.open(). An attacker can submit a small-dimension GIF containing many comment extensions to any application that opens the file. This can consume excessive CPU and deny service before the application can inspect the image. 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, affecting versions from 5.4.0 through 13.0.0, represents a significant performance degradation flaw rooted in inefficient memory management and algorithmic complexity during GIF file parsing. Specifically, the issue resides in the GifImageFile._seek() method, which is responsible for navigating through different frames or extensions within a GIF structure. When processing comment sub-blocks and multiple comment extensions embedded within a GIF image, the library employs quadratic copying behavior. This means that as the parser iterates through these elements, it repeatedly appends immutable bytes to existing data structures rather than utilizing more efficient memory allocation strategies such as pre-allocation or in-place modification where appropriate. Because frame zero is parsed during the initial Image.open() call, this inefficient processing occurs immediately upon file access, triggering the resource exhaustion before any application-level inspection of the image content can take place.
From a technical perspective, the core flaw involves an O(n^2) time complexity relative to the number of comment extensions or sub-blocks present in the GIF file. In standard software engineering practices, handling sequential data with immutable strings often leads to this pattern if not carefully managed using builders or byte arrays that support efficient concatenation. By repeatedly creating new string objects and discarding old ones during the parsing loop, Pillow generates substantial garbage collection overhead and CPU cycles. An attacker can exploit this by crafting a GIF file with small dimensions but containing an excessive number of comment extensions. Since the computational cost scales quadratically rather than linearly, even moderately sized malicious files can cause disproportionate resource consumption compared to their actual data size. This design flaw allows for efficient denial-of-service attacks against any application that integrates Pillow and processes user-uploaded or untrusted image inputs without prior validation.
The operational impact of this vulnerability is primarily centered on availability, as the excessive CPU usage caused by the quadratic copying can lead to service degradation or complete unresponsiveness in affected applications. This aligns with Common Weakness Enumeration (CWE) category CWE-400: Uncontrolled Resource Consumption, specifically highlighting issues related to algorithmic complexity leading to resource exhaustion. Furthermore, this vulnerability maps directly to MITRE ATT&CK technique T1496: Resource Hijacking, where adversaries leverage computational resources of a target system for disruptive purposes rather than data exfiltration or persistence. The attack vector is straightforward and does not require authentication if the application automatically processes uploaded images, making it particularly dangerous in web services that accept image uploads for profile pictures, document attachments, or media galleries.
Mitigation strategies must focus on both immediate patching and defensive coding practices to prevent recurrence. The primary remediation is upgrading Pillow to version 13.0.0 or later, where the underlying parsing logic has been optimized to eliminate the quadratic copying behavior. For environments unable to upgrade immediately, implementing strict input validation at the application layer is critical. This includes limiting the maximum file size of uploaded GIFs and potentially restricting the number of frames or extensions allowed within a single image structure. Additionally, integrating third-party libraries like libvips for initial image inspection can provide an additional layer of defense by parsing images outside of Pillow's main processing pipeline before they are handed over to vulnerable components. Organizations should also monitor CPU usage metrics in production environments to detect anomalies that may indicate active exploitation attempts targeting this specific resource exhaustion vector.