CVE-2021-41202 is a medium-severity vulnerability found in Google TensorFlow. This vulnerability allows potential overflows to occur when calculating the size of the output within the `tf.range` kernel. Specifically, the issue arises from a conditional statement that is affected by C++ implicit conversion rules, which can lead to erroneous behavior and impact the application's stability.
The CVSS 3.1 score for this vulnerability is 5.5, indicating a medium severity level. Organizations should prioritize addressing this vulnerability, especially since it has a high impact on availability, as the application may crash due to overflows.
The vulnerability was published on November 5, 2021, and the affected versions include TensorFlow from 2.4.0 up to but not including 2.4.4, as well as 2.5.0 up to but not including 2.5.2, and 2.6.0 up to but not including 2.6.1. The fix for this issue is included in TensorFlow version 2.7.0.
Due to the nature of this vulnerability, organizations using TensorFlow should apply the necessary patches promptly to avoid potential disruptions.
Vulnerability Details
The official description of CVE-2021-41202 states that TensorFlow is an open-source platform for machine learning. In affected versions, a conditional statement in the `tf.range` kernel leads to truncation due to implicit type conversion, resulting in potential overflows.
The vulnerability has a CVSS score of 5.5, which reflects its medium severity. This score indicates a moderate risk to organizations, particularly in terms of application availability.
TensorFlow versions 2.4.0 through 2.4.4, 2.5.0 through 2.5.2, and 2.6.0 through 2.6.1 are affected. The fixed versions include TensorFlow 2.7.0 and later.
Technical Analysis
The root cause of this vulnerability lies in how C++ handles implicit conversions. The condition in the `tf.range` kernel leads to both branches being cast to a `double`, which can cause a truncation error before assignment, resulting in integer overflows.
The attack vector for this vulnerability is local, meaning that an attacker would need local access to exploit it. The attack complexity is low, and it requires low privileges with no user interaction necessary. The impacts on confidentiality and integrity are none, but the availability impact is high.
Risk & Impact Analysis
Risk to organizations includes potential application crashes, which can disrupt services and affect user experience. The fact that this vulnerability can lead to availability issues means that organizations must act swiftly to mitigate risks.
Given the CVSS score of 5.5, organizations should assess their patching strategy to prioritize the update of TensorFlow installations. The swift implementation of patches can help to minimize the potential blast radius associated with this vulnerability.
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: 2.4.0 through 2.4.4, 2.5.0 through 2.5.2, and 2.6.0 through 2.6.1. Organizations using these versions should upgrade to at least TensorFlow 2.7.0 to mitigate the vulnerability.
Mitigation & Remediation
Organizations should prioritize patching immediately. The fix for CVE-2021-41202 will be included in TensorFlow 2.7.0. For those on older versions, it is recommended to upgrade to TensorFlow 2.6.1, 2.5.2, or 2.4.4, where available. Configuration hardening and continuous monitoring should also be considered.
In addition to applying patches, organizations may consider implementing network controls to limit access to affected systems and monitoring logs for unusual activity related to TensorFlow operations.
Penetration testing can further help validate the effectiveness of the applied remediation measures.
Detection Guidance
To detect potential exploitation of this vulnerability, organizations should monitor for log indicators associated with TensorFlow operations, especially those that involve the `tf.range` kernel. Any unusual application crashes or performance degradation should be investigated promptly.
Monitoring for behavioral anomalies and system changes associated with TensorFlow use can provide valuable insights into potential exploitation attempts.
AppSecure Threat Intelligence Insight
The long-term significance of CVE-2021-41202 highlights the importance of rigorous testing and validation in software development lifecycles, particularly for open-source platforms like TensorFlow.
This vulnerability serves as a reminder for security teams to assess their development practices and ensure that proper safeguards are in place to prevent similar issues in the future.
Organizations should also consider conducting regular security assessments and reviews, focusing on the integration of security testing into their development processes. For more guidance on this topic, refer to our blog on penetration testing methodology and other best practices.
The pattern represented by this vulnerability underscores the critical need for ongoing vigilance and proactive measures to safeguard applications against potential threats.
Known Exploitation Timeline
As of now, CVE-2021-41202 has not been included in the KEV catalog, indicating that it is not currently known to be actively exploited in the wild.
EPSS Risk Context
The EPSS score of 0.00037 places CVE-2021-41202 in the 0.11 percentile. This low score indicates a very low probability of exploitation in the near term. Nevertheless, it is crucial for organizations to remain vigilant and apply patches promptly.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
.webp)