CVE-2022-35995 is a medium-severity vulnerability identified in TensorFlow, an open-source machine learning platform widely used for developing and deploying machine learning models. The vulnerability arises from a reachable assertion failure (CWE-617) within the AudioSummaryV2 component of TensorFlow. Specifically, when the AudioSummaryV2 function receives an input parameter 'sample_rate' containing more than one element, it triggers a CHECK failure, which is an assertion designed to validate input assumptions during runtime. This assertion failure causes the TensorFlow process to terminate unexpectedly, leading to a denial of service (DoS) condition. The vulnerability affects multiple TensorFlow versions: all versions prior to 2.7.2, versions from 2.8.0 up to but not including 2.8.1, and versions from 2.9.0 up to but not including 2.9.1. The issue has been addressed in GitHub commit bf6b45244992e2ee543c258e519489659c99fb7f and will be included in TensorFlow 2.10.0, with backported fixes planned for 2.7.2, 2.8.1, and 2.9.1. There are currently no known workarounds for this vulnerability, and no exploits have been observed in the wild to date. The vulnerability requires an attacker to supply a malformed input to the AudioSummaryV2 function, which may be possible in environments where untrusted data is processed by TensorFlow models or services. However, no authentication or user interaction is explicitly required to trigger the assertion failure once the vulnerable function is invoked with crafted input.

For European organizations leveraging TensorFlow in their machine learning pipelines, especially those processing audio data or utilizing the AudioSummaryV2 functionality, this vulnerability can lead to denial of service conditions. This can disrupt critical AI-driven services, including voice recognition, audio analysis, and other machine learning applications, potentially causing downtime or degraded service availability. In sectors such as finance, healthcare, telecommunications, and automotive industries—where AI and machine learning are increasingly integrated—such disruptions could impact operational continuity and service reliability. Although the vulnerability does not directly compromise confidentiality or integrity, the availability impact could indirectly affect business processes and user trust. Since TensorFlow is widely used in research institutions and enterprises across Europe, unpatched systems remain susceptible to crashes triggered by malformed inputs, which could be exploited in targeted attacks or accidental failures. The absence of known exploits reduces immediate risk, but the vulnerability's presence in supported TensorFlow versions means that organizations must act promptly to mitigate potential exposure.

European organizations should prioritize upgrading TensorFlow installations to versions 2.7.2, 2.8.1, 2.9.1, or later, where the vulnerability has been patched. Since no workarounds exist, patching is the primary mitigation strategy. Additionally, organizations should implement strict input validation and sanitization on all data fed into TensorFlow models, particularly for audio-related inputs, to prevent malformed 'sample_rate' parameters from reaching the vulnerable function. Deploying runtime monitoring and anomaly detection to identify unexpected crashes or assertion failures in TensorFlow services can help detect exploitation attempts early. For environments where immediate patching is not feasible, isolating TensorFlow workloads processing untrusted audio data within sandboxed containers or restricted execution environments can limit the impact of potential crashes. Finally, organizations should review their machine learning deployment pipelines to ensure that only trusted or validated data sources are used, reducing the attack surface for triggering this vulnerability.