CVE-2022-41890 affects Google TensorFlow, an open-source platform for machine learning. This vulnerability allows TensorFlow to crash when the `BCast::ToShape` function is given input larger than an `int32`. Despite the expectation that it can handle inputs up to an `int64`, providing excessively large inputs leads to unexpected behavior. This issue is observable in the `tf.experimental.numpy.outer` function when large inputs are passed to the input parameter `b`. The vulnerability has been patched in GitHub commit 8310bf8dd188ff780e7fc53245058215a05bdbe5, which will be included in TensorFlow version 2.11. Additionally, this fix will be cherry-picked for TensorFlow versions 2.10.1, 2.9.3, and 2.8.4, all of which are still within the supported range.
The vulnerability has been assigned a CVSS score of 4.8, categorized as medium severity. The attack vector is categorized as network, and it requires low privileges and user interaction. The impact on availability is high, which indicates that successful exploitation could render the affected services unavailable. Risk to organizations includes potential service disruptions leading to downtime or degraded performance.
As this vulnerability falls under the medium severity category, organizations should prioritize patching during their next maintenance cycle. It is crucial to ensure that all instances of TensorFlow are updated to the latest supported versions to mitigate the risks associated with this vulnerability.
The urgency for defenders is moderate, and organizations are advised to schedule remediation. Given the potential for high availability impact, timely updates are essential to maintain service continuity.
Vulnerability Details
The official CVE description states that this vulnerability allows TensorFlow to crash when the `BCast::ToShape` function receives input larger than an `int32`. The function is expected to handle inputs up to an `int64`. The affected TensorFlow versions include those prior to 2.8.4, as well as 2.9.0 through 2.10.1. The vulnerability is classified under CWE-704.
The vulnerability has significant implications for organizations using TensorFlow for critical machine learning tasks, especially where stability and availability are paramount.
Technical Analysis
The root cause of this vulnerability lies in the failure of the `BCast::ToShape` function to handle large inputs appropriately. When inputs exceed the anticipated size, it results in a crash, which can compromise the stability of applications relying on TensorFlow. The attack vector is primarily through network interactions, where an attacker can input excessively large data to trigger this condition.
The attack complexity is classified as high, as it requires specific conditions to be met for successful exploitation, including low privileges and user interaction. No additional user actions are needed once the input is provided to the function. The confidentiality and integrity impacts are rated as none, but the availability impact remains high, emphasizing the need for immediate remediation.
Risk & Impact Analysis
The real-world deployment risk associated with CVE-2022-41890 is notable. Organizations utilizing TensorFlow in production environments need to be aware of the potential for service disruption due to crashes triggered by large inputs. The blast radius could affect any application relying on TensorFlow, illustrating the importance of prompt patching.
Risk to organizations includes service downtime and subsequent financial impacts, particularly for applications providing critical services or customer-facing functionalities. The urgency for organizations to address this vulnerability is moderate, and they should incorporate patching into their regular update cycles.
Exploitation Status
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
The versions of TensorFlow affected by this vulnerability include all versions prior to 2.8.4, as well as versions 2.9.0 through 2.10.1. Organizations should ensure their TensorFlow installations are updated accordingly to mitigate the risk.
Mitigation & Remediation
Organizations should prioritize applying the patch available in TensorFlow version 2.11 or later. For those using earlier versions, the patch from GitHub commit 8310bf8dd188ff780e7fc53245058215a05bdbe5 should be cherry-picked to ensure the vulnerability is resolved. Additionally, organizations should implement configuration hardening and monitoring to detect any abnormal behavior that may indicate an attempt to exploit this vulnerability.
Detection Guidance
Organizations should monitor their TensorFlow environments for logs indicating crashes or unexpected behavior, especially when handling large inputs. Behavioral anomalies during operation could also serve as indicators of exploitation attempts.
AppSecure Threat Intelligence Insight
The long-term significance of CVE-2022-41890 lies in its exposure of TensorFlow's handling of large inputs, which can lead to unexpected crashes. This pattern highlights the need for thorough testing and validation of input sizes in machine learning applications, particularly those deployed in critical environments.
Security teams should take this incident as a lesson in the importance of proactive vulnerability management. Regular patching and testing are essential strategies to minimize the risk of similar vulnerabilities in the future. For organizations considering the implementation of robust security measures, application security assessments are highly recommended.
Furthermore, organizations should consider engaging in continuous penetration testing to ensure their systems remain resilient against emerging threats.
Overall, the incident underscores the importance of vigilance in maintaining the integrity and availability of machine learning services.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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