CVE-2022-23567 is a medium-severity vulnerability affecting TensorFlow, an open-source machine learning framework widely used for developing and deploying machine learning models. The vulnerability arises from integer overflow issues in the implementations of the Sparse*Cwise* operations within TensorFlow. Specifically, the problem lies in insufficient validation of input tensor shapes and the direct construction of large TensorShape objects using user-provided dimensions. These integer overflows can lead to the allocation of excessively large memory buffers, resulting in out-of-memory (OOM) conditions that cause denial of service (DoS). Additionally, the vulnerability can trigger CHECK-failures (assertion failures) during TensorShape construction, also leading to DoS by crashing the application. The vulnerability affects multiple supported TensorFlow versions, including 2.5.3, 2.6.3, 2.7.1, and will be fixed in 2.8.0. The CVSS v3.1 base score is 6.5 (medium), with an attack vector of network (remote), low attack complexity, requiring low privileges but no user interaction, and impacting availability only. No known exploits are currently reported in the wild. The root cause is classified under CWE-190 (Integer Overflow or Wraparound). This vulnerability is significant because machine learning workloads often process untrusted or external data, and an attacker could craft malicious inputs to trigger these overflows, causing service interruptions or crashes in systems relying on TensorFlow for inference or training tasks.

For European organizations, the impact of CVE-2022-23567 primarily concerns availability disruptions in machine learning services that utilize vulnerable TensorFlow versions. Organizations in sectors such as finance, healthcare, automotive, telecommunications, and research institutions that deploy TensorFlow-based models for critical applications could experience denial of service conditions, leading to downtime, degraded service quality, or interruption of automated decision-making processes. This could affect customer-facing services, internal analytics, or safety-critical systems relying on ML inference. While confidentiality and integrity are not directly impacted, the availability loss could have cascading operational and reputational consequences. Given the increasing adoption of AI/ML technologies across Europe, especially in countries with strong AI research and industrial sectors, the risk of disruption is non-negligible. However, exploitation requires the ability to supply crafted input tensors to the vulnerable operations, which may limit the attack surface to exposed ML inference endpoints or pipelines processing untrusted data.

European organizations should promptly upgrade TensorFlow to version 2.8.0 or apply the backported patches available for versions 2.5.3, 2.6.3, and 2.7.1 to remediate the vulnerability. In addition to patching, organizations should implement strict input validation and sanitization on all tensor data received from external or untrusted sources to prevent malformed inputs that could trigger integer overflows. Deploying runtime monitoring to detect abnormal memory allocation patterns or frequent assertion failures in ML services can help identify exploitation attempts early. Where possible, restrict access to ML model endpoints through network segmentation, authentication, and authorization controls to reduce exposure. For critical systems, consider sandboxing TensorFlow execution environments to contain potential crashes or resource exhaustion. Finally, maintain an inventory of TensorFlow deployments and versions to ensure comprehensive coverage of patching efforts.