CVE-2022-35959 is a vulnerability in TensorFlow, an open-source machine learning platform widely used for developing and deploying machine learning models. The flaw exists in the implementation of the AvgPool3DGradOp operation, which is responsible for computing gradients during the backpropagation phase of 3D average pooling layers. Specifically, the vulnerability arises because the input parameter orig_input_shape is not fully validated, leading to an integer overflow. This overflow triggers a CHECK failure, which is an assertion mechanism used internally by TensorFlow to verify assumptions during execution. When this assertion fails, it causes the process to terminate abruptly, resulting in a denial of service (DoS) condition. The vulnerability affects TensorFlow versions prior to 2.7.2, as well as specific minor versions 2.8.0 to 2.8.1 and 2.9.0 to 2.9.1. The issue has been patched in TensorFlow 2.10.0 and backported to supported versions 2.7.2, 2.8.1, and 2.9.1. No known workarounds exist, and no exploits have been observed in the wild to date. The vulnerability is classified under CWE-617 (Reachable Assertion), indicating that an attacker can trigger an assertion failure that leads to process termination. The attack vector requires supplying crafted input data to the vulnerable operation, which could be feasible in environments where untrusted data is processed by TensorFlow models. The impact is limited to denial of service, with no indication of code execution or data corruption. The vulnerability does not require authentication but does require the attacker to influence the input data processed by TensorFlow.

For European organizations leveraging TensorFlow in their machine learning pipelines, this vulnerability poses a risk of service disruption. Organizations using TensorFlow for critical applications such as healthcare diagnostics, financial modeling, or industrial automation could experience outages or degraded service availability if an attacker supplies malicious input triggering the assertion failure. Since TensorFlow is often integrated into larger systems, a denial of service in the ML component could cascade, impacting dependent services or delaying critical decision-making processes. The vulnerability does not appear to compromise data confidentiality or integrity directly, but availability impacts could affect operational continuity and reliability. Given the absence of known exploits, the immediate risk is moderate; however, organizations processing untrusted or external data streams with TensorFlow should be particularly cautious. The lack of workarounds means patching is the primary mitigation strategy. Failure to patch could leave systems vulnerable to targeted DoS attacks, especially in environments exposed to external inputs or multi-tenant cloud deployments where adversaries might attempt to disrupt services.

1. Immediate upgrade to TensorFlow versions 2.7.2, 2.8.1, 2.9.1, or later (including 2.10.0) that contain the patch for CVE-2022-35959. 2. Implement strict input validation and sanitization on all data fed into TensorFlow models, especially for 3D average pooling operations, to reduce the risk of malformed inputs triggering the vulnerability. 3. Employ runtime monitoring and anomaly detection to identify unexpected TensorFlow process terminations or assertion failures, enabling rapid incident response. 4. For cloud or containerized deployments, use orchestration tools to automatically restart TensorFlow services upon failure to minimize downtime. 5. Isolate TensorFlow workloads processing untrusted data in sandboxed or restricted environments to limit the blast radius of potential DoS attacks. 6. Review and harden access controls to TensorFlow model endpoints to prevent unauthorized or malicious input submissions. 7. Maintain an up-to-date inventory of TensorFlow versions deployed across the organization to ensure timely patch management. 8. Engage with TensorFlow community and security advisories to stay informed about any emerging exploits or additional mitigations.