CVE-2022-21741 is a medium-severity vulnerability affecting TensorFlow's TFLite (TensorFlow Lite) implementation, specifically in the depthwise convolution operation. TensorFlow is a widely used open-source machine learning framework, and TFLite is its lightweight version optimized for mobile and embedded devices. The vulnerability arises because the parameters controlling the depthwise convolution, which are user-controllable, are used in a division operation to calculate the padding size before applying the convolution. There is no validation to ensure that the divisor is strictly positive, leading to a potential division by zero error. This flaw can be triggered by an attacker crafting a malicious TFLite model that exploits this unchecked division. The consequence of this division by zero is a denial of service (DoS) condition, causing the affected process to crash or become unstable. The vulnerability does not impact confidentiality or integrity but affects availability. It requires the attacker to have the ability to supply or influence the TFLite model being processed, which implies some level of privilege or access to the environment where TensorFlow is running. The issue affects multiple supported TensorFlow versions, including 2.5.3, 2.6.3, 2.7.1, and will be fixed in 2.8.0. No known exploits are reported in the wild. The CVSS v3.1 base score is 6.5 (medium), reflecting network attack vector, low complexity, low privileges required, no user interaction, unchanged scope, no impact on confidentiality or integrity, but high impact on availability. The underlying weakness is classified as CWE-369 (Divide By Zero).

For European organizations, the primary impact of CVE-2022-21741 is the potential for denial of service in applications or services that utilize TensorFlow Lite for machine learning inference. This could disrupt critical AI-driven functionalities in sectors such as automotive (e.g., autonomous driving systems), healthcare (e.g., diagnostic tools), manufacturing (e.g., predictive maintenance), and mobile applications. Since TensorFlow Lite is often embedded in edge devices and mobile platforms, the vulnerability could lead to crashes or service interruptions, affecting operational continuity. While there is no direct data breach or integrity compromise, availability issues can degrade user trust and cause financial or reputational damage. Organizations deploying TensorFlow models from untrusted sources or allowing user-supplied models are at higher risk. The lack of known exploits reduces immediate threat, but the widespread use of TensorFlow in European tech ecosystems means that unpatched systems could be targeted in the future. Additionally, critical infrastructure or research institutions relying on AI models may experience disruptions if adversaries exploit this flaw.

European organizations should take the following specific steps to mitigate CVE-2022-21741: 1) Upgrade TensorFlow to version 2.8.0 or later, or apply the relevant patches to versions 2.5.3, 2.6.3, or 2.7.1 as soon as possible to ensure the division by zero check is implemented. 2) Implement strict validation and sanitization of all input TFLite models, especially those sourced from external or untrusted parties, to prevent maliciously crafted models from triggering the vulnerability. 3) Employ runtime monitoring and anomaly detection to identify unexpected crashes or instability in TensorFlow-based services, enabling rapid incident response. 4) Restrict the ability to upload or execute user-supplied models to trusted users or isolated environments to reduce attack surface. 5) Conduct thorough testing of AI applications post-patch to confirm stability and absence of regressions. 6) Maintain an inventory of all systems and applications using TensorFlow Lite to prioritize patching and risk assessment. 7) Collaborate with AI and cybersecurity teams to integrate secure development lifecycle practices for machine learning components.