CVE-2022-41902 is a high-severity vulnerability in Google TensorFlow, which is an open source platform for machine learning. This vulnerability allows an attacker to exploit the function MakeGrapplerFunctionItem, which takes arguments that determine the sizes of inputs and outputs. If the inputs given are greater than or equal to the sizes of the outputs, an out-of-bounds memory read or a crash is triggered. The severity is classified as high due to the potential for serious impacts.
The CVSS score for this vulnerability is 7.1, indicating a high severity level. This score reflects the potential impact on confidentiality, integrity, and availability, with all three categorized as high. Organizations using affected versions of TensorFlow should prioritize patching immediately to prevent exploitation.
As of now, there are no confirmed public exploits available. However, the vulnerability's existence poses a risk to organizations, particularly those in critical use cases. The urgency for defenders is underscored by the potential for significant operational disruption if exploited.
Organizations should be aware that the fix for this issue has been implemented in GitHub commit a65411a1d69edfb16b25907ffb8f73556ce36bb7. The patch will be included in TensorFlow version 2.11.0, and it will also be cherrypicked for versions 2.8.4, 2.9.3, and 2.10.1. Therefore, timely action is essential.
In summary, given the high severity of CVE-2022-41902 and its potential impact, organizations should address this vulnerability as part of their urgent patch management efforts.
Vulnerability Details
The vulnerability is characterized by out-of-bounds memory access issues triggered by the MakeGrapplerFunctionItem function in TensorFlow. The official description states that an out-of-bounds memory read or a crash is triggered when the inputs are greater than or equal to the output sizes. This has been classified under CWE-787 and CWE-125.
The CVSS version 3.1 vector string is CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:H/I:H/A:H, indicating that the attack vector is network-based, with high attack complexity, and requires low privileges and user interaction.
Technical Analysis
The root cause of this vulnerability lies in the improper handling of input and output sizes within the MakeGrapplerFunctionItem function. Attackers may leverage this flaw to perform out-of-bounds memory reads, potentially leading to information disclosure or application crashes.
The attack vector for this vulnerability is network-based, requiring low privileges and user interaction. Given the complexity involved in exploiting this vulnerability, it poses a moderate to high risk to organizations using vulnerable versions of TensorFlow.
Risk & Impact Analysis
Risk to organizations includes potential data leaks or application crashes, which can disrupt services and impact business operations. The vulnerability's blast radius could be significant, especially in environments where TensorFlow is deployed for critical machine learning tasks.
Organizations should assess their exposure to this vulnerability and prioritize remediation efforts based on the severity rating of 7.1. The potential for exploitation and the operational impact underscores the need for immediate attention.
Exploitation Status
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 the vendor patch, specifically versions before 2.8.4, as well as versions 2.9.0 to 2.9.3 and 2.10.0.
Mitigation & Remediation
Organizations should update TensorFlow to version 2.11.0 or later to mitigate this vulnerability. If immediate upgrading is not feasible, it is recommended to apply the patch from GitHub commit a65411a1d69edfb16b25907ffb8f73556ce36bb7. Additionally, organizations may consider implementing network controls and configuration hardening as interim mitigations.
For further information on security practices, consider reviewing the resources available on penetration testing and other security assessments.
Detection Guidance
Log indicators of potential exploitation may include unusual memory access patterns and application crashes. Monitoring for behavioral anomalies in TensorFlow deployments can help in early detection of this vulnerability being exploited.
AppSecure Threat Intelligence Insight
The long-term significance of CVE-2022-41902 indicates the importance of rigorous testing and validation in machine learning frameworks. Security teams should prioritize the integration of security assessments into their development processes, particularly for widely-used libraries like TensorFlow.
This vulnerability highlights the need for ongoing monitoring and proactive security measures in technology stacks. For more insights on assessing vulnerabilities in machine learning environments, you may find the following resources helpful: AI security best practices, penetration testing methodology, and vulnerability management program design 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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