CVE-2021-41214 is a high-severity vulnerability affecting Google TensorFlow, an open-source platform for machine learning. The issue arises from the shape inference code for `tf.ragged.cross`, which contains an undefined behavior due to binding a reference to `nullptr`. This vulnerability poses a significant risk as it could lead to unauthorized access or data corruption. It is crucial for organizations using affected versions of TensorFlow to address this vulnerability promptly.
The CVSS score for this vulnerability is 7.8, indicating a high level of severity. The attack vector is classified as local, and the attack complexity is low, meaning that an attacker with low privileges can exploit this vulnerability without requiring user interaction. The impact on confidentiality, integrity, and availability is assessed as high, making this a critical vulnerability that requires immediate attention.
Organizations should prioritize patching immediately. The fix for this vulnerability will be included in TensorFlow 2.7.0, and it will also be cherry-picked into earlier versions 2.6.1, 2.5.2, and 2.4.4, which are still in the supported range. Failure to apply the appropriate updates may result in exploitation of this vulnerability, leading to severe consequences.
This vulnerability highlights the importance of maintaining up-to-date software and applying security patches in a timely manner. Organizations relying on TensorFlow should ensure they have a robust vulnerability management program in place to address risks associated with software vulnerabilities.
Vulnerability Details
According to the official CVE description, this vulnerability allows for undefined behavior in the shape inference code for `tf.ragged.cross`. The affected versions of TensorFlow include all versions prior to the fixed version in 2.7.0, specifically versions 2.4.4, 2.5.0, and 2.6.0. The CWE classification for this vulnerability is CWE-824, indicating a reference to a null pointer.
Technical Analysis
The root cause of this vulnerability is linked to improper handling of pointers in the TensorFlow codebase, specifically within the shape inference function of the `tf.ragged.cross` method. This issue can lead to unpredictable behavior, potentially allowing an attacker to manipulate the program's execution. The attack vector for this vulnerability is local, requiring the attacker to have access to the system where TensorFlow is running. The complexity of exploiting this vulnerability is low, indicating that it is relatively straightforward for an attacker to execute.
No user interaction is required for exploitation, further increasing the risk. The vulnerability has high impacts on confidentiality, integrity, and availability, which means that successful exploitation could lead to unauthorized access to sensitive information, data corruption, or service disruption.
Risk & Impact Analysis
Risk to organizations includes the potential for unauthorized access, data corruption, and service disruptions. The ease of exploitation, combined with the high impact of the vulnerabilities, makes it critical for organizations to take this threat seriously. Given that TensorFlow is widely used in machine learning applications, the blast radius of this vulnerability could affect many systems and services relying on TensorFlow libraries.
With a CVSS score of 7.8, organizations should address this vulnerability in their priority patch cycle. The urgency is underscored by the fact that this vulnerability is now publicly known, increasing the likelihood of exploitation by malicious actors. Organizations must ensure they monitor their TensorFlow installations and apply patches promptly to mitigate potential risks.
Exploitation Status
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
All versions of TensorFlow prior to 2.7.0 are affected by this vulnerability. Specifically, the vulnerable versions include 2.4.4, 2.5.0, and 2.6.0. It is recommended that users upgrade to TensorFlow 2.7.0 or later to mitigate the risks associated with this vulnerability.
Mitigation & Remediation
To remediate this vulnerability, organizations should upgrade to TensorFlow version 2.7.0 or later, where the fix has been implemented. For those using earlier versions, it is advisable to cherry-pick the relevant commit from TensorFlow's repository to ensure a patch is applied. If immediate upgrading is not possible, organizations should consider implementing configuration hardening measures to limit the impact of potential exploitation.
Additionally, organizations may benefit from engaging in continuous security testing to identify any weaknesses in their implementation of TensorFlow. Regular monitoring and application of security patches should be a part of the operational security strategy.
Detection Guidance
Organizations should monitor log indicators for any unexpected behavior within TensorFlow applications. Behavioral anomalies, such as crashes or unusual resource usage patterns, may signal exploitation attempts. Additionally, network signatures related to TensorFlow operations should be analyzed for potential signs of compromise.
AppSecure Threat Intelligence Insight
The long-term significance of CVE-2021-41214 lies in its representation of vulnerabilities inherent in complex machine learning frameworks. As TensorFlow continues to evolve, it is imperative for security teams to remain vigilant against potential vulnerabilities that could arise due to the complexity of the codebase.
This incident serves as a reminder for organizations to adopt security best practices, such as regular vulnerability assessments and patch management. Security teams should leverage insights from past vulnerabilities, like CVE-2021-41214, to enhance their defensive strategies and improve their incident response capabilities.
For further guidance on improving security posture, organizations can explore our resources on penetration testing and ongoing security assessments.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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