CVE-2022-21738 is a medium-severity vulnerability affecting Google TensorFlow, an open-source machine learning framework. This vulnerability allows TensorFlow processes to crash due to an integer overflow that occurs during the execution of the `SparseCountSparseOutput` function. Specifically, the overflow can lead to a negative value being used in a memory allocation, resulting in application instability. The CVSS 3.1 score for this vulnerability is 6.5, indicating a medium risk level that should not be overlooked.
Risk to organizations includes potential service disruptions and the inability to perform machine learning tasks effectively. Given the importance of TensorFlow in various AI and machine learning applications, this vulnerability presents a significant threat to enterprises relying on this technology. Organizations should prioritize patching this vulnerability immediately to prevent unexpected crashes and maintain operational continuity.
As of now, there are no known exploits publicly available for this vulnerability, and it has not been included in the Known Exploited Vulnerabilities (KEV) catalog. However, the potential for exploitation remains a concern, and organizations should take this seriously. The fix for this vulnerability is scheduled for inclusion in TensorFlow version 2.8.0, with backports to versions 2.7.1, 2.6.3, and 2.5.3 for affected users still within the support range.
To mitigate risks, organizations should implement the forthcoming patches and monitor for updates related to this vulnerability. Ensuring that systems are up to date will significantly reduce the chances of encountering issues related to this integer overflow vulnerability.
Vulnerability Details
The official description of CVE-2022-21738 highlights that the implementation of `SparseCountSparseOutput` can lead to a crash due to an integer overflow. The vulnerability has a CVSS score of 6.5, categorized as medium severity. The affected product is TensorFlow, developed by Google, with the vulnerability being published on February 3, 2022. The Common Weakness Enumeration (CWE) classification for this vulnerability is CWE-190.
Technical Analysis
The root cause of CVE-2022-21738 is an integer overflow that occurs in the `SparseCountSparseOutput` function. The attack vector for this vulnerability is network-based, with low attack complexity, requiring low privileges and no user interaction. The availability impact is classified as high, meaning that successful exploitation could lead to significant service disruption. However, there are no impacts on confidentiality or integrity.
Risk & Impact Analysis
Real-world deployment of TensorFlow with this vulnerability poses a risk of application crashes, which can halt machine learning processes crucial for various business operations. The blast radius potential is significant, affecting all users of the vulnerable TensorFlow versions. Given the medium severity score and the potential for operational disruption, organizations should address this vulnerability in their priority patch cycle.
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
The affected versions of TensorFlow include all versions prior to 2.5.3, specifically versions 2.5.0 through 2.5.2, 2.6.0 to 2.6.2, and version 2.7.0. Organizations using these versions should prepare to upgrade to patch the vulnerability.
Mitigation & Remediation
Organizations should ensure they are running the latest version of TensorFlow, specifically upgrading to version 2.8.0 or later to mitigate this vulnerability. If immediate upgrading is not possible, consider applying any available workarounds or configuration hardening to limit exposure. Regular monitoring for updates and patches is essential. For a comprehensive security approach, consider integrating penetration testing to identify any additional weaknesses in your implementation.
Detection Guidance
To detect potential exploitation of this vulnerability, organizations should monitor logs for any abnormal application crashes or memory allocation failures. Behavioral anomalies during the execution of TensorFlow processes may also indicate attempts to exploit this vulnerability. Implementing network signatures that can identify unusual traffic patterns could help in early detection.
AppSecure Threat Intelligence Insight
The long-term significance of CVE-2022-21738 lies in its representation of vulnerabilities that can arise from resource management issues in machine learning frameworks. As organizations increasingly rely on AI, understanding and mitigating such risks becomes critical. Security teams should take this opportunity to assess their overall risk posture and implement robust practices to manage vulnerabilities effectively. For further guidance on maintaining application security, consider reviewing our resources on vulnerability management programs and strategies for penetration testing methodologies to enhance your security framework.
In summary, CVE-2022-21738 is a notable vulnerability that organizations using TensorFlow must address promptly to ensure the stability and security of their machine learning applications.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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