CVE-2021-37673 is a medium-severity vulnerability affecting Google TensorFlow, an end-to-end open-source platform for machine learning. The vulnerability allows attackers to trigger a denial of service by exploiting a CHECK-fail in the `tf.raw_ops.MapStage` operation. Specifically, the implementation does not validate that the `key` input is a valid non-empty tensor, which can lead to significant disruptions.
The Common Vulnerability Scoring System (CVSS) v3.1 has assigned this vulnerability a score of 5.5, indicating a medium severity level. The risk to organizations includes potential downtime and resource unavailability, particularly in sensitive applications that rely on TensorFlow for processing. As this vulnerability can be triggered locally, it poses a tangible threat to systems running affected versions of TensorFlow.
The TensorFlow team has addressed this vulnerability in GitHub commit d7de67733925de196ec8863a33445b73f9562d1d. The fix will be included in TensorFlow version 2.6.0 and will also be cherry-picked into versions 2.5.1, 2.4.3, and 2.3.4, as these versions are still within the support range. Organizations using TensorFlow should prioritize applying these patches to mitigate the risk.
Given the nature of this vulnerability and the potential for exploitation, organizations should prioritize patching immediately to ensure the integrity and availability of their machine learning applications.
Vulnerability Details
The official description of this vulnerability states that it allows an attacker to trigger a denial of service due to a CHECK-fail in `tf.raw_ops.MapStage`. The vulnerability is classified under CWE-20, indicating improper input validation.
The CVSS score is 5.5, with a base severity of medium. The attack vector is categorized as LOCAL, and the attack complexity is LOW, requiring low privileges with no user interaction. This increases the urgency for organizations to address this vulnerability.
Affected versions include TensorFlow from 2.3.0 to 2.6.0 (release candidates included). The vulnerability was published on August 12, 2021, and has since been modified in the advisory details.
Technical Analysis
The root cause of this vulnerability lies in the lack of input validation for the `key` tensor in the MapStage operation. Attackers can exploit this oversight by providing an empty tensor, which results in a CHECK-fail and potentially crashes the service.
The attack vector is local, meaning that the attacker must have access to the system where TensorFlow is running. The complexity of the attack is low, as it does not require sophisticated techniques or high-level privileges, making it accessible for attackers with basic knowledge of TensorFlow operations.
No user interaction is required to exploit this vulnerability, which further increases the risk. The impact on availability is high, as the service can become unavailable if exploited.
Risk & Impact Analysis
The potential impact of CVE-2021-37673 is significant for organizations leveraging TensorFlow in production environments. A successful exploit can lead to service disruptions, impacting business operations and user experience.
The blast radius of this vulnerability is particularly concerning given TensorFlow's widespread use in machine learning applications across various industries. Organizations must assess their deployment of TensorFlow and the associated risks.
Given the CVSS score of 5.5, organizations should address this vulnerability in their priority patch cycle, ensuring that they apply the necessary updates to mitigate risk.
Exploitation Status
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
Affected versions include TensorFlow 2.3.0 to 2.3.4, 2.4.0 to 2.4.3, and 2.5.0. Additionally, the release candidates of TensorFlow 2.6.0 (rc0, rc1, rc2) are also vulnerable. Organizations should ensure they upgrade to the patched versions to mitigate the risks associated with this vulnerability.
Mitigation & Remediation
To remediate this vulnerability, organizations should upgrade to TensorFlow version 2.6.0 or later. For those using earlier versions, it is crucial to apply the patches available in GitHub commit d7de67733925de196ec8863a33445b73f9562d1d. If immediate upgrading is not possible, consider implementing additional input validation on the tensor inputs to prevent empty tensor submissions.
For those seeking assistance with security measures, organizations can consider engaging in penetration testing to identify potential weaknesses in their TensorFlow implementations.
Detection Guidance
Organizations should monitor logs for unusual activity related to TensorFlow operations, particularly those involving the MapStage function. Additionally, behavioral anomalies indicating service downtime may serve as indicators of potential exploitation.
Network signatures that correlate with TensorFlow operations may also assist in detecting attempts to exploit this vulnerability.
AppSecure Threat Intelligence Insight
CVE-2021-37673 highlights the importance of rigorous input validation in software development, particularly in machine learning frameworks where data integrity is paramount.
Security teams should take this opportunity to review their TensorFlow implementations and ensure proper validation mechanisms are in place to prevent similar vulnerabilities. Implementing a comprehensive security strategy that includes regular code audits and penetration testing can greatly minimize risks.
For further insights on securing TensorFlow applications, organizations can explore our AI security best practices and engage with our penetration testing methodology for a proactive approach to identifying and mitigating vulnerabilities.
Organizations should also consider how emerging patterns in vulnerabilities can impact their security posture and adapt their strategies accordingly.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
.webp)