CVE-2026-112395 in Pillow
Summary
by MITRE • 10/20/2026
Pillow is a Python imaging library. From 8.2.0 until 13.0.0, Pillow's tiled-TIFF decoder can read past the tile buffer while copying four-byte pixels. In _decodeTile, libtiff can return a tile allocation smaller than the required row stride multiplied by the tile length, and Pillow's stride-and-copy loop walks beyond that allocation. An attacker can supply a crafted tiled TIFF to an application that loads it. This can expose adjacent heap data in pixels 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 as CVE-2024-47689 represents a critical out-of-bounds read flaw within Pillow, a widely used Python imaging library that serves as a foundational component for numerous web applications and data processing pipelines. The issue resides specifically in the tiled-TIFF decoder implementation, affecting versions from 8.2.0 through 13.0.0. This vulnerability stems from an improper boundary check during the decoding process of TIFF files that utilize tiling compression schemes. When Pillow processes such a file, it relies on libtiff to allocate memory for individual tiles and then iterates over these allocations to copy pixel data into the final image buffer. The core technical flaw occurs in the _decodeTile function where the library fails to validate whether the allocated tile size is sufficient to accommodate the calculated row stride multiplied by the tile length. Consequently, when a crafted tiled TIFF file with malformed dimensions or metadata is processed, the decoding loop proceeds without verifying that the destination memory region remains within the bounds of the allocated buffer.
From a technical perspective, this defect allows an attacker to trigger a heap-based out-of-bounds read operation. Because the stride-and-copy loop walks beyond the boundaries of the tile allocation, it reads adjacent memory regions on the heap. This behavior directly maps to CWE-125, which describes Out-of-Bounds Read vulnerabilities where software reads data past the end or before the beginning of a buffer. The impact of this flaw is twofold: first, it can lead to information disclosure by exposing sensitive heap data that resides adjacent to the image tile in memory. This exposed data may contain pointers, cryptographic keys, session tokens, or other private information depending on what else was allocated nearby at runtime. Second, if the read operation accesses unmapped or protected memory pages, it will cause a segmentation fault, resulting in a denial of service by crashing the application process. In environments where Pillow is used to handle user-uploaded images, such as social media platforms or document management systems, this vulnerability can be exploited remotely without authentication.
The operational impact extends beyond immediate crashes or data leaks due to the potential for further exploitation techniques that rely on memory corruption primitives. Although this specific instance is classified primarily as an out-of-bounds read rather than a write, reading arbitrary heap contents provides attackers with valuable reconnaissance capabilities. By analyzing the leaked bytes, an attacker can potentially bypass address space layout randomization protections or identify other vulnerable objects in memory to chain attacks together. This aligns with ATT&CK technique T1083, File and Directory Discovery, as well as T1567, Exfiltration Over Web Service if the data is subsequently transmitted out of the victim environment. The severity is heightened by the ubiquity of Pillow; many high-profile frameworks like Django and Flask integrate it for image processing tasks, meaning a single vulnerable application can serve as an entry point to compromise entire backend infrastructure.
Mitigation strategies must prioritize immediate version upgrades alongside defensive coding practices. The most effective remediation is upgrading Pillow to version 13.0.0 or later, where the developers have implemented strict bounds checking before executing the tile copy operations. For organizations unable to upgrade immediately due to dependency constraints, implementing a web application firewall rule that inspects incoming TIFF files for anomalous tiling parameters can provide temporary relief, though this is not a substitute for patching. Additionally, running image processing tasks in isolated sandboxed environments with restricted memory access limits can mitigate the impact of successful exploitation by limiting the amount of sensitive data exposed or preventing process crashes from affecting critical system components. Regular security audits and dependency scanning should be integrated into the software development lifecycle to ensure that third-party libraries like Pillow remain up-to-date, thereby reducing the attack surface associated with known vulnerabilities in image parsing engines.