CVE-2022-23567 is a medium-severity vulnerability identified within Google TensorFlow, an Open Source Machine Learning Framework. The vulnerability arises from the implementations of `Sparse*Cwise*` operations that are susceptible to integer overflows. This vulnerability allows attackers to trigger large memory allocations, potentially leading to out-of-memory (OOM) based denial of service (DoS) attacks. Alternatively, it may cause `CHECK` failures during the construction of new `TensorShape` objects, resulting in assertion failures that can also lead to denial of service.
The issue stems from a lack of validation on the shapes of input tensors and the direct construction of a large `TensorShape` using user-provided dimensions. This oversight poses a risk to organizations utilizing TensorFlow in production environments. The fix for this vulnerability is scheduled to be included in TensorFlow version 2.8.0, with backporting planned for earlier versions, specifically TensorFlow 2.7.1, 2.6.3, and 2.5.3, which are also affected.
Organizations should prioritize patching immediately. Failure to address this vulnerability could lead to significant service disruptions and operational challenges.
This vulnerability has been classified under CWE-190, which identifies integer overflow as a critical risk factor. The CVSS score for this vulnerability is 6.5, indicating a medium level of severity that requires attention from security teams.
Vulnerability Details
The CVSS vector for CVE-2022-23567 is CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H. It indicates that the attack vector is network-based, with low complexity and low privileges required, and it does not require user interaction. The only impact identified is high availability impact, which can lead to service outages.
Technical Analysis
The root cause of this vulnerability lies in the insufficient validation mechanisms for input tensor shapes in TensorFlow's implementation of Sparse*Cwise* operations. Attackers can exploit this flaw to create large input tensors that exceed expected limits, leading to integer overflows. The attack vector is network-based, allowing remote attackers to trigger the vulnerability without requiring physical access to the target system. The complexity of the attack is low, as it involves crafting input that can easily be sent over the network.
As the attack does not require user interaction, it can be executed without any involvement from legitimate users. The availability impact is classified as high, meaning that successful exploitation can cause significant downtime for the affected services.
Risk & Impact Analysis
Organizations using TensorFlow should assess the risk associated with this vulnerability in their environments. Given the widespread use of TensorFlow for machine learning applications, the potential blast radius of exploitation is substantial. An attacker could exploit this vulnerability to disrupt services, leading to downtime and potential data loss.
The urgency to address this vulnerability is underscored by its CVSS score of 6.5, indicating a medium severity that organizations should not overlook. The vulnerability is not part of the Known Exploited Vulnerabilities (KEV) catalog, which means that while there is no active exploitation reported, the risk remains significant.
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
The vulnerable versions of TensorFlow include all versions prior to 2.8.0, specifically those up to 2.5.2, as well as versions 2.6.0 through 2.6.2 and 2.7.0. Organizations should ensure they update to the patched versions to mitigate this vulnerability.
Mitigation & Remediation
To address CVE-2022-23567, organizations should patch their TensorFlow installations to version 2.8.0 or later. For those using affected earlier versions, cherrypicked fixes are available for TensorFlow 2.7.1, 2.6.3, and 2.5.3. If immediate patching is not feasible, organizations should implement strong input validation on tensors and monitor for any anomalous behavior that could indicate exploitation attempts. Additionally, network controls can help limit exposure to this vulnerability.
For further guidance, organizations can refer to the methodology for effective penetration testing to validate their security posture.
Detection Guidance
Organizations should monitor logs for unusual patterns that may indicate attempts to exploit this vulnerability. Specific indicators include large tensor size requests and unexpected application crashes.
AppSecure Threat Intelligence Insight
CVE-2022-23567 highlights the importance of rigorous input validation in software development. The trends observed in this vulnerability emphasize the need for proactive security measures in machine learning frameworks. Security teams should prioritize regular updates to their libraries and frameworks, ensuring they are aware of the latest security advisories.
For organizations utilizing TensorFlow, understanding the impact of vulnerabilities like this one is crucial. Adopting a comprehensive security strategy that includes regular penetration testing methodology can help identify potential weaknesses before they can be exploited.
Additionally, leveraging tools that facilitate vulnerability management programs can significantly enhance an organization’s security posture against similar vulnerabilities in the future.
Finally, incorporating lessons learned from incidents involving vulnerabilities like CVE-2022-23567 into security training programs will better prepare teams to handle future threats.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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