CVE-2022-35973 describes a vulnerability in TensorFlow, an open-source platform for machine learning. When the `QuantizedMatMul` operation is provided with nonscalar inputs for parameters such as `min_a`, `max_a`, `min_b`, or `max_b`, it may lead to a segmentation fault. This issue can be exploited to launch a denial of service (DoS) attack, affecting the availability of the application. The severity of this vulnerability is classified as medium, with a CVSS score of 5.9, indicating that it poses a notable risk to organizations relying on this software.
The vulnerability has been addressed in GitHub commit aca766ac7693bf29ed0df55ad6bfcc78f35e7f48. The fix will be included in TensorFlow version 2.10.0, and the patch will also be cherrypicked into versions 2.9.1, 2.8.1, and 2.7.2, which are still under support. Given the nature of this vulnerability, it is crucial for organizations to prioritize patching to maintain the security and availability of their deployments.
Currently, there are no known workarounds for this issue, making it imperative for users to apply the patches as soon as they become available. Organizations should assess their use of TensorFlow and prepare to implement the necessary updates to mitigate the risk associated with this vulnerability.
In summary, organizations using TensorFlow must remain vigilant and ensure that they upgrade to the patched versions to avoid potential service disruption due to this vulnerability.
Vulnerability Details
The official description of this vulnerability states that it arises in the TensorFlow platform when the `QuantizedMatMul` function receives nonscalar inputs for specific parameters, leading to a segmentation fault. The CVSS score from the NVD is 7.5, which indicates a high severity level, while the GitHub advisory lists it as medium with a score of 5.9.
The vulnerability affects all versions of TensorFlow prior to the patches being applied. The specific configurations impacted include TensorFlow versions 2.7.0 to 2.7.2, 2.8.0 to 2.8.1, and 2.9.0 to 2.9.1, as well as the release candidates for version 2.10.
Technical Analysis
The root cause of this vulnerability lies in the handling of input parameters for the `QuantizedMatMul` operation. The operation does not adequately validate the input types, allowing for nonscalar values, which ultimately leads to a segmentation fault when processing these inputs.
The attack vector for this vulnerability is network-based, with a high attack complexity, meaning that while it requires specific conditions to exploit, it does not necessitate elevated privileges or user interaction, making it an attractive target for attackers. The availability impact is high, as successful exploitation can lead to service downtime.
Risk & Impact Analysis
Risk to organizations includes potential denial of service, leading to service interruptions that can affect business operations and customer trust. The blast radius extends to any application utilizing TensorFlow that does not implement the necessary patches. Given the increasing reliance on machine learning frameworks, the urgency for organizations to address this vulnerability is heightened, especially since the availability impact is rated as high.
The CVSS score indicates a medium severity; however, the potential for exploitation in a networked environment means organizations should evaluate the risk and prioritize remediation efforts accordingly. Organizations that utilize TensorFlow in production should act promptly to ensure that they apply the patches as soon as they are available.
Exploitation Status
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
The following TensorFlow versions are affected by this vulnerability: 2.7.0 to 2.7.2, 2.8.0 to 2.8.1, 2.9.0 to 2.9.1, and the release candidates for version 2.10 (rc0, rc1, rc2, rc3). Organizations should update to the patched versions as they become available.
Mitigation & Remediation
Organizations must prioritize patching TensorFlow to versions 2.10.0 or later. The specific commits addressing this vulnerability can be found in the GitHub repository. For those unable to immediately upgrade, consider implementing network controls to limit exposure to the vulnerable components until a patch can be applied. Continuous monitoring for abnormal behavior can also help in early detection of potential exploitation attempts.
For further information on penetration testing and continuous security assessments, organizations are encouraged to explore penetration testing services to identify further vulnerabilities in their deployment.
Detection Guidance
Organizations should monitor logs for any unusual patterns that may indicate exploitation attempts. Behavioral anomalies, such as unexpected service crashes or performance degradation, should be investigated promptly. Network signatures associated with denial of service attacks should also be utilized to enhance detection capabilities.
AppSecure Threat Intelligence Insight
The long-term significance of CVE-2022-35973 lies in the reliance on machine learning platforms like TensorFlow, which are increasingly adopted in various applications. The pattern of vulnerabilities in such frameworks underlines the critical importance of robust input validation and error handling to prevent service disruptions.
Security teams should take note of this vulnerability as a reminder to regularly assess application security, especially for components that handle external inputs. The lessons learned from this incident demonstrate the need for thorough testing and validation processes during development.
Ultimately, organizations must prioritize ongoing education and training in secure coding practices to prevent similar vulnerabilities in the future. For more insights into secure development practices, organizations can refer to the resource on secure coding practices and implement comprehensive application security assessments.
Additionally, organizations can strengthen their security posture by engaging in penetration testing methodology to identify vulnerabilities proactively and ensure resilience against potential threats.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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