CVE-2023-52355 is a vulnerability identified in libtiff, a widely used open-source library for reading and writing TIFF image files. The flaw is an out-of-bounds write caused by an out-of-memory condition triggered when the TIFFRasterScanlineSize64() API processes a specially crafted TIFF file. Specifically, the vulnerability occurs when the input TIFF file size is smaller than 379 KB, which leads to improper memory handling and potential corruption. This can be exploited remotely by an attacker who supplies a malicious TIFF file to an application or service that uses libtiff for image processing. The vulnerability does not require any privileges or user interaction, making it easier to exploit in automated or network-facing scenarios. The primary impact is denial of service, as the out-of-bounds write can cause application crashes or instability. Although no known exploits have been reported in the wild, the CVSS 3.1 score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H) indicates a high severity due to the remote attack vector and significant availability impact. The vulnerability affects all versions of libtiff prior to the patch release, and no specific vendor or product is named, implying that any software embedding libtiff is potentially vulnerable. The flaw highlights the importance of validating input sizes and ensuring robust memory management in image processing libraries.

For European organizations, the primary impact of CVE-2023-52355 is the risk of denial of service in systems that process TIFF images using libtiff. This includes web servers, document management systems, digital forensics tools, and media processing pipelines. A successful attack could disrupt business operations, cause downtime, and impact service availability, especially in sectors relying heavily on image data such as publishing, healthcare, and government archives. The lack of confidentiality or integrity impact reduces the risk of data breaches, but availability disruptions can still have significant operational and reputational consequences. Organizations with automated workflows that ingest TIFF files from external or untrusted sources are particularly vulnerable. The absence of known exploits in the wild reduces immediate risk but does not eliminate the threat, as proof-of-concept exploits could emerge. The vulnerability also poses a risk to embedded systems or IoT devices in Europe that use libtiff for image processing, potentially affecting critical infrastructure components.

1. Monitor official libtiff repositories and vendor advisories for patches addressing CVE-2023-52355 and apply updates promptly. 2. Until patches are available, implement input validation to block or sandbox TIFF files smaller than 379 KB or those from untrusted sources. 3. Employ application-layer protections such as limiting the processing of TIFF files to trusted users or internal networks. 4. Use memory safety tools and runtime protections (e.g., AddressSanitizer, Control Flow Integrity) during development and testing to detect and prevent out-of-bounds writes. 5. For web-facing applications, consider implementing web application firewalls (WAFs) with custom rules to detect and block malformed TIFF payloads. 6. Conduct security reviews and code audits of any custom software integrating libtiff to ensure safe usage of TIFFRasterScanlineSize64() and related APIs. 7. Educate staff handling image ingestion workflows about the risks of processing untrusted TIFF files. 8. Maintain robust incident response procedures to quickly mitigate denial of service events if exploitation occurs.