CVE-2022-41910 is a medium-severity vulnerability affecting Google TensorFlow, an open-source platform for machine learning. The vulnerability arises from the function MakeGrapplerFunctionItem, which takes arguments that determine the sizes of inputs and outputs. If the inputs provided are greater than or equal to the sizes of the outputs, it can trigger an out-of-bounds memory read or cause a crash. This issue was patched in GitHub commit a65411a1d69edfb16b25907ffb8f73556ce36bb7. The fix will be included in TensorFlow 2.11.0 and will also be cherry-picked to versions 2.8.4, 2.9.3, and 2.10.1.
The vulnerability has a CVSS score of 4.8, indicating a medium severity level, which means organizations should address it in their priority patch cycle. The risk to organizations includes potential downtime and system instability caused by the crash. While there is no known exploit or public proof-of-concept, the nature of the vulnerability implies that it could be leveraged by attackers to disrupt services or gain unauthorized access.
Given the potential impact of this vulnerability, organizations utilizing TensorFlow should prioritize patching their installations to the latest version as soon as it is available. The urgency for defenders cannot be overstated, as unresolved vulnerabilities can lead to greater risks.
For further details, refer to the official advisory and the GitHub commit linked in the references section. Continuous monitoring for updates and patches is essential to maintain a secure environment.
Vulnerability Details
The official CVE description states that this vulnerability allows an out-of-bounds memory read or crash due to improper input handling in TensorFlow's MakeGrapplerFunctionItem function. The vulnerability is classified under CWE-125, indicating improper input validation.
The CVSS 3.1 score of 4.8 reflects a medium severity classification, with a specific vector string indicating a network attack vector, high complexity, and low privileges required. The availability impact is rated high, while confidentiality and integrity impacts are none.
The vulnerability affects Google TensorFlow and was published on December 6, 2022. Organizations should ensure they are running versions later than 2.11.0 or the patched versions of 2.8.4, 2.9.3, and 2.10.1.
Technical Analysis
The root cause of CVE-2022-41910 lies in the improper handling of input sizes in the MakeGrapplerFunctionItem function within TensorFlow. The attack vector is network-based, requiring low privileges to exploit, but does necessitate user interaction. The attack complexity is rated as high, indicating that successfully executing an attack may require significant skill or effort.
Regarding impacts, the vulnerability does not affect confidentiality or integrity; however, it poses a high availability risk due to potential crashes from out-of-bounds memory reads.
Risk & Impact Analysis
The real-world deployment risk associated with this vulnerability is significant, particularly for organizations using TensorFlow in production environments. The potential for crashes can lead to service disruptions, affecting user experience and trust. Furthermore, the lack of known exploitation does not diminish the urgency; attackers often search for such vulnerabilities to exploit in their campaigns.
Organizations should evaluate their use of TensorFlow and assess the potential blast radius if this vulnerability were to be exploited. Given the CVSS score of 4.8, this vulnerability should be included in priority patch cycles to mitigate risks effectively.
Urgency for remediation is medium, as organizations should schedule remediation in their upcoming patch cycles to ensure they remain secure against potential availability-related threats.
Exploitation Status
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
The following versions of TensorFlow are affected by this vulnerability:
1. All versions prior to the vendor patch for TensorFlow 2.8.4. 2. TensorFlow 2.9.0 to 2.9.3. 3. TensorFlow 2.10.0.
Mitigation & Remediation
Organizations should prioritize patching their installations of TensorFlow to the fixed version, which is TensorFlow 2.11.0. If immediate patching is not possible, consider implementing configuration hardening and strict input validation to mitigate potential risks until the patch can be applied.
For a comprehensive approach to security, organizations may also want to consider engaging in penetration testing to validate the effectiveness of their remediation efforts.
Detection Guidance
To detect potential exploitation of this vulnerability, organizations should monitor for unusual log entries or behavior anomalies in TensorFlow applications. Additionally, analyze network traffic for any signs of malicious interaction with TensorFlow services.
AppSecure Threat Intelligence Insight
The long-term significance of CVE-2022-41910 lies in its demonstration of the risks associated with improper input validation in widely-used machine learning platforms. This vulnerability highlights the necessity for organizations to regularly update their software and maintain a proactive security posture.
Security teams should learn from this incident to enhance their input validation mechanisms and ensure that all inputs are properly sanitized before processing. For further reading on best practices in securing machine learning platforms, organizations can refer to the following resources:
1. API security best practices 2. Penetration testing methodology 3. Vulnerability management program design
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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