CVE-2022-35992 describes a vulnerability in TensorFlow, an open-source platform for machine learning. This vulnerability allows TensorListFromTensor to trigger a denial of service (DoS) when it receives an element_shape of a rank greater than one, leading to a CHECK fail. The vulnerability has a CVSS score of 5.9, categorizing it as medium severity. Given the potential for a denial of service, organizations should prioritize patching immediately to safeguard their systems.
The issue was first published on September 16, 2022, and has since been modified with a patch included in TensorFlow version 2.10.0. The patch will also be applied to TensorFlow versions 2.9.1, 2.8.1, and 2.7.2, which are still supported. There are currently no known workarounds for this vulnerability, making it imperative for users to upgrade to the patched versions.
With the increasing adoption of machine learning frameworks, the exploitation of such vulnerabilities can lead to significant service interruptions. Organizations utilizing TensorFlow should assess their environments for affected versions and ensure timely updates to mitigate the risks associated with this vulnerability.
Given the potential impact on availability, this vulnerability should be treated with a medium urgency level, necessitating inclusion in regular security assessments and patch cycles.
Vulnerability Details
TensorFlow is an open-source platform for machine learning. When TensorListFromTensor receives an element_shape of a rank greater than one, it gives a CHECK fail that can trigger a denial of service attack. We have patched the issue in GitHub commit 3db59a042a38f4338aa207922fa2f476e000a6ee. The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range. There are no known workarounds for this issue.
Technical Analysis
The root cause of this vulnerability stems from improper handling of input shapes in the TensorListFromTensor function. Specifically, the function fails to validate the rank of the input shape, leading to a CHECK fail, which can be exploited to cause a denial of service.
The attack vector is network-based, with a high attack complexity due to the specific conditions required to trigger the vulnerability. No privileges are required to exploit this vulnerability, and user interaction is not needed. The availability impact is rated as high, as successful exploitation can lead to service disruptions.
Risk & Impact Analysis
The real-world risk associated with CVE-2022-35992 involves potential denial of service attacks that can disrupt the functionality of applications relying on TensorFlow. If exploited, this vulnerability could lead to significant downtime and loss of service availability, affecting business operations and user experiences. As TensorFlow is widely used in various sectors, the blast radius is considerable.
Organizations should evaluate their risk posture regarding this vulnerability and prioritize remediation efforts. Given the medium severity of this vulnerability and the high availability impact, it should be included in the priority patch cycle.
The urgency for addressing this vulnerability is moderate. Organizations should schedule remediation as part of their regular security management processes.
Exploitation Status
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
The affected versions of TensorFlow are 2.7.2 and prior, as well as 2.8.0 to 2.8.1, and 2.9.0 to 2.9.1. Additionally, the release candidates for TensorFlow 2.10.0 (rc0 to rc3) are also vulnerable. Organizations should upgrade to the latest patched version to mitigate risks.
Mitigation & Remediation
Organizations should prioritize patching TensorFlow to version 2.10.0 or later to remediate this vulnerability. If immediate patching is not feasible, organizations should consider implementing network controls to limit exposure to this vulnerability until a patch can be applied. Regular monitoring for unusual service disruptions should also be conducted.
For continuous security, organizations may find value in conducting regular penetration testing to identify potential vulnerabilities and ensure security measures are effective.
Detection Guidance
Organizations should monitor logs for signs of unusual behavior associated with TensorFlow operations. Specific indicators include service crashes, unexpected restarts, and high CPU usage patterns that could suggest an attempted denial of service attack.
AppSecure Threat Intelligence Insight
The long-term significance of CVE-2022-35992 highlights the importance of maintaining rigorous input validation in machine learning frameworks. As these technologies become foundational in various applications, vulnerabilities that allow denial of service can have far-reaching impacts.
Security teams should analyze existing frameworks to identify similar weaknesses and develop strategies to mitigate such risks proactively. Regular updates and security assessments are essential for maintaining resilience against emerging threats.
For further reading on vulnerability management and security best practices, refer to the following resources: vulnerability management program and penetration testing methodology to enhance your security posture.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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