CVE-2014-9601 in Pillow
Summary
by MITRE
Pillow before 2.7.0 allows remote attackers to cause a denial of service via a compressed text chunk in a PNG image that has a large size when it is decompressed.
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Analysis
by VulDB Data Team • 04/11/2022
The vulnerability identified as CVE-2014-9601 affects the Pillow library, a popular Python imaging library that serves as a fork of the Python Imaging Library PIL. This flaw exists in versions prior to 2.7.0 and represents a significant denial of service vulnerability that can be exploited remotely through crafted PNG image files. The vulnerability specifically targets the decompression process of PNG images, where maliciously constructed compressed text chunks can trigger excessive resource consumption during decompression operations. This issue falls under the category of resource exhaustion attacks that can overwhelm system resources and render applications unavailable to legitimate users.
The technical mechanism behind this vulnerability involves the improper handling of compressed text chunks within PNG image files during the decompression phase. When Pillow processes a PNG image containing a specially crafted compressed text chunk, the decompression algorithm encounters a compressed data structure that expands to an unexpectedly large size upon decompression. This creates a scenario where the decompression process consumes disproportionate amounts of memory and processing time, leading to system resource exhaustion. The vulnerability demonstrates characteristics consistent with CWE-400, which addresses improper handling of resource constraints, and more specifically aligns with CWE-129, concerning improper validation of array indices. The flaw operates at the intersection of compression algorithm handling and memory management within the image processing pipeline.
From an operational perspective, this vulnerability poses a serious threat to web applications and services that process user-uploaded PNG images through the Pillow library. Attackers can craft malicious PNG files that, when processed by vulnerable applications, cause denial of service conditions that may result in application crashes, system instability, or complete service unavailability. The remote exploitation aspect means that attackers do not need physical access to the target system and can leverage this vulnerability through web interfaces or any application that accepts and processes PNG images. This vulnerability can be particularly dangerous in environments where image processing is a core function, such as content management systems, social media platforms, e-commerce sites, and any application that handles user-generated image content. The impact extends beyond simple service disruption to potentially affect the entire availability of critical business applications.
Mitigation strategies for CVE-2014-9601 primarily involve upgrading to Pillow version 2.7.0 or later, which contains the necessary patches to properly handle compressed text chunks and prevent excessive resource consumption during decompression. Organizations should implement comprehensive patch management procedures to ensure all affected systems are updated promptly. Additional protective measures include implementing strict image validation and sanitization processes that can detect and reject potentially malicious image files before they reach the decompression stage. Network-level protections such as rate limiting and resource quotas can help mitigate the impact of exploitation attempts. The vulnerability also highlights the importance of input validation and resource monitoring in image processing applications, aligning with ATT&CK technique T1499.004 for resource exhaustion attacks. Security teams should also consider implementing automated scanning and monitoring tools that can detect anomalous resource consumption patterns that may indicate exploitation attempts. Organizations should conduct thorough testing of patched versions to ensure that the security fixes do not introduce compatibility issues with existing image processing workflows.