CVE-2021-39432 is a vulnerability identified in diplib version 3.0.0, characterized as a Double Free flaw (CWE-416). A double free vulnerability occurs when a program attempts to free the same memory location twice, which can lead to undefined behavior including program crashes, memory corruption, or potential execution of arbitrary code. In this case, the vulnerability affects diplib, an open-source library used for scientific image processing and analysis. The CVSS 3.1 base score for this vulnerability is 6.5 (medium severity), with the vector indicating network attack vector (AV:N), low attack complexity (AC:L), no privileges required (PR:N), but requiring user interaction (UI:R). The impact is limited to availability (A:H), with no confidentiality or integrity impact (C:N/I:N). This suggests that exploitation could cause denial of service (DoS) conditions by crashing or destabilizing applications using diplib, but does not allow for data leakage or modification. No known exploits in the wild have been reported, and no patches or vendor information are provided, indicating that users of diplib 3.0.0 should be cautious and monitor for updates. The vulnerability was published on November 4, 2022, and was reserved in August 2021. The lack of vendor or product details complicates direct attribution but the technical nature points to diplib as the affected component. The requirement for user interaction implies that exploitation might occur through crafted input files or user-triggered processing of malicious data within applications leveraging diplib.

For European organizations, the primary impact of CVE-2021-39432 lies in potential service disruption due to denial of service attacks targeting applications that incorporate diplib 3.0.0. Organizations in sectors relying on scientific image processing—such as healthcare (medical imaging), research institutions, and industrial quality control—may experience operational interruptions if attackers exploit this vulnerability. Although the vulnerability does not compromise confidentiality or integrity, availability degradation can affect critical workflows, delay diagnostics, or halt automated image analysis pipelines. Given that exploitation requires user interaction, phishing or social engineering could be vectors to deliver malicious input files. The absence of known exploits reduces immediate risk, but the medium severity score and ease of network-based exploitation without privileges mean that organizations should proactively assess their exposure. The impact is more pronounced in environments where diplib is integrated into larger systems without robust input validation or sandboxing, increasing the risk of cascading failures.

To mitigate CVE-2021-39432, European organizations should: 1) Identify and inventory all software and systems using diplib, especially version 3.0.0, within their environments. 2) Apply strict input validation and sanitization on all image data processed by diplib to prevent malformed or malicious files from triggering the double free condition. 3) Implement application-level sandboxing or containerization to isolate diplib-dependent processes, limiting the impact of potential crashes. 4) Monitor for updates or patches from diplib maintainers or community repositories and apply them promptly once available. 5) Educate users on the risks of opening untrusted image files or data inputs, reducing the likelihood of user interaction-based exploitation. 6) Employ runtime protections such as AddressSanitizer or similar memory error detection tools during development and testing to detect double free conditions early. 7) Incorporate robust logging and monitoring to detect abnormal application terminations or crashes that may indicate exploitation attempts. These targeted measures go beyond generic advice by focusing on the specific nature of the vulnerability and the operational context of diplib usage.