CVE-2025-48174 is a medium-severity vulnerability identified in the libavif library, specifically in versions prior to 1.3.0. Libavif is an open-source library used for encoding and decoding AVIF images, which are based on the AV1 video codec and increasingly used for high-efficiency image compression on the web and in multimedia applications. The vulnerability arises from an integer overflow in the makeRoom function within the stream.c source file. This function attempts to allocate or reserve buffer space by calculating the sum of stream->offset and size. Due to insufficient validation, this addition can overflow the integer variable, causing the computed size to wrap around to a smaller value than intended. Consequently, this leads to a buffer overflow when the program writes beyond the allocated memory region. The vulnerability affects the confidentiality, integrity, and availability of applications using libavif, as a buffer overflow can potentially be exploited to execute arbitrary code, cause crashes, or corrupt memory. However, the CVSS vector indicates that the attack requires local access (AV:L), has high attack complexity (AC:H), does not require privileges (PR:N), and no user interaction (UI:N). The scope is changed (S:C), meaning the vulnerability can affect resources beyond the vulnerable component. The impact on confidentiality is none, but there is low impact on integrity and availability. No known exploits are currently in the wild, and no patches have been linked yet. Given the widespread adoption of libavif in web browsers, image processing tools, and multimedia applications, this vulnerability poses a risk to any software that processes AVIF images using vulnerable versions of libavif.

For European organizations, the impact of CVE-2025-48174 depends largely on their use of libavif in their software stacks. Organizations involved in web services, digital media, content delivery, and software development that handle AVIF images are at risk. Exploitation could allow attackers with local access to cause application crashes or potentially execute arbitrary code, leading to service disruptions or data integrity issues. This is particularly concerning for sectors with high reliance on multimedia content such as media companies, online retailers, and digital marketing firms. Additionally, embedded systems or IoT devices using libavif could be affected, potentially impacting industrial or critical infrastructure environments. Since the attack requires local access and has high complexity, remote exploitation is less likely, but insider threats or compromised internal systems could leverage this vulnerability. The absence of known exploits reduces immediate risk, but the vulnerability's presence in a widely used library means that once exploit code is developed, rapid exploitation could occur. European organizations must consider this vulnerability in their risk assessments, especially those with large-scale multimedia processing or custom software incorporating libavif.

To mitigate CVE-2025-48174, European organizations should: 1) Identify all software and systems using libavif, especially versions prior to 1.3.0. 2) Monitor for official patches or updates from the aomedia project and apply them promptly once available. 3) Until patches are released, consider implementing input validation and sanitization for AVIF images to detect malformed or suspicious files that could trigger the overflow. 4) Employ runtime protections such as Address Space Layout Randomization (ASLR), Data Execution Prevention (DEP), and stack canaries to reduce the impact of buffer overflows. 5) Limit local access to systems processing AVIF images to trusted users only, reducing the risk of local exploitation. 6) Use application whitelisting and endpoint detection and response (EDR) tools to detect anomalous behavior potentially related to exploitation attempts. 7) For developers, review and harden code that interacts with libavif, adding bounds checking and error handling around buffer allocations. 8) Conduct security testing and fuzzing on AVIF image processing components to identify similar or related vulnerabilities proactively.