CVE-2022-41894 is a medium-severity classic buffer overflow vulnerability in TensorFlow, specifically within the TensorFlow Lite operator `CONV_3D_TRANSPOSE` reference kernel. TensorFlow is a widely used open-source machine learning platform. The vulnerability arises from an incorrect pointer arithmetic operation when adding bias to the convolution result. The code increments the data pointer by the number of input channels (`num_channels`) instead of the number of output channels (`output_num_channels`). When the number of input channels exceeds the number of output channels, this leads to a buffer overflow, allowing an attacker to write beyond the allocated buffer boundaries. Exploitation requires an attacker to craft a malicious TensorFlow Lite model with a specific number of input channels to trigger the overflow via the bias addition. This vulnerability only affects the reference kernel resolver used in the TensorFlow Lite interpreter, meaning it is limited to environments where this specific kernel implementation is active. The issue affects TensorFlow versions prior to 2.8.4, versions from 2.9.0 up to but not including 2.9.3, and versions from 2.10.0 up to but not including 2.10.1. The vulnerability has been patched in TensorFlow 2.11 and backported to supported versions 2.8.4, 2.9.3, and 2.10.1. No known exploits have been reported in the wild, indicating limited or no active exploitation to date. The root cause is a classic buffer overflow (CWE-120), which can compromise memory safety and potentially lead to arbitrary code execution or denial of service if exploited. However, exploitation requires the attacker to supply a malicious model to the vulnerable TensorFlow Lite interpreter, which may limit attack vectors to scenarios where untrusted models are loaded or executed.

For European organizations, the impact of this vulnerability depends largely on their use of TensorFlow Lite with the reference kernel resolver in environments where untrusted or user-supplied models might be processed. Organizations leveraging TensorFlow for machine learning inference on edge devices, mobile applications, or embedded systems could be at risk if they accept or process externally sourced models without validation. Successful exploitation could lead to memory corruption, potentially allowing attackers to execute arbitrary code, disrupt machine learning services, or cause denial of service. This could impact critical sectors such as automotive (autonomous driving systems), healthcare (medical imaging analysis), finance (fraud detection), and industrial automation, where TensorFlow Lite is deployed. The confidentiality, integrity, and availability of machine learning workloads could be compromised, leading to data breaches, manipulation of ML outputs, or operational disruptions. However, the requirement to use the reference kernel resolver and the need for an attacker to supply a malicious model limits the scope somewhat. Organizations that do not expose model loading interfaces or that validate models before execution are less likely to be impacted. Additionally, since no known exploits exist in the wild, the immediate risk is moderate but should not be ignored given the potential severity of buffer overflow vulnerabilities.

1. Upgrade TensorFlow to version 2.11 or later, or apply the backported patches available in versions 2.8.4, 2.9.3, and 2.10.1 to ensure the buffer overflow is fixed. 2. Restrict the sources of TensorFlow Lite models to trusted and verified origins only; implement strict model validation and integrity checks before loading models into the interpreter. 3. Avoid using the reference kernel resolver in production environments if possible; consider using alternative kernel implementations that are not affected by this vulnerability. 4. Employ runtime protections such as memory safety tools, address space layout randomization (ASLR), and control flow integrity (CFI) to mitigate exploitation impact. 5. Monitor TensorFlow Lite usage and model loading activities for anomalies that could indicate attempts to exploit this vulnerability. 6. For organizations deploying TensorFlow Lite on edge or embedded devices, implement secure update mechanisms to rapidly deploy patches. 7. Conduct security reviews and penetration testing focused on machine learning model ingestion pipelines to identify and remediate potential attack vectors related to untrusted model inputs.