The CVE-2021-29602 affects TensorFlow, an open-source platform for machine learning. This vulnerability allows for a division by zero error in the implementation of the DepthwiseConv TFLite operator. An attacker can craft a model such that the input's fourth dimension would be 0, leading to potential application crashes or other unintended behaviors. Considering this vulnerability's low severity, organizations should nevertheless take the necessary steps to mitigate risks.
The CVSS score for this vulnerability is 2.5, classified as low severity. This score reflects the potential impact on the availability of the application, as the availability impact is rated as low. However, organizations should not underestimate the risk, as the exploitation could lead to application instability.
Currently, there is no known exploit for this vulnerability. However, organizations should remain vigilant and monitor for updates. The fix for this issue will be included in TensorFlow version 2.5.0 and cherrypicked into earlier supported versions.
Organizations should prioritize scheduling remediation efforts to address this vulnerability in their TensorFlow deployments.
Vulnerability Details
The official description of CVE-2021-29602 highlights that the implementation of the DepthwiseConv TFLite operator is vulnerable to a division by zero error. This vulnerability can affect the availability of the application when an attacker crafts a specific model with an input dimension of zero.
The CVSS score from the NVD assigns a medium severity of 5.5, which reflects the potential for significant impact on availability. The attack vector is local, and the attack complexity is considered low, meaning that it can be exploited with minimal skill. Additionally, the vulnerability requires low privileges and does not necessitate user interaction.
The vulnerability is classified under CWE-369, indicating that improper handling of input can lead to unexpected behaviors. The flaw exists in TensorFlow's DepthwiseConv implementation, which is widely used in machine learning applications.
Technical Analysis
The root cause of CVE-2021-29602 lies in the implementation of the DepthwiseConv operator within the TensorFlow framework. When the input's fourth dimension is zero, it triggers a division by zero error, leading to potential application crashes. This vulnerability can be exploited locally, as it does not require any network access or user interaction.
The attack complexity is rated as low, implying that attackers can exploit this vulnerability with minimal effort. The required privileges are also low, meaning that a user with basic access could potentially trigger the flaw. However, there is no impact on confidentiality or integrity, as the primary concern is the application's availability.
Risk & Impact Analysis
Risk to organizations includes potential application instability and downtime, which could affect critical machine learning workflows. With the availability impact rated low, the urgency to address this vulnerability is moderate. Organizations using affected TensorFlow versions should schedule remediation efforts to prevent service disruptions.
Given that there is no active exploitation reported, organizations can prioritize this vulnerability in their upcoming patch cycles. However, they should stay informed about the release of the patched TensorFlow versions.
The overall risk is manageable, but organizations should remain cautious and ensure that their TensorFlow implementations are updated promptly.
Exploitation Status
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
All versions of TensorFlow prior to 2.5.0 are affected, including TensorFlow 2.4.2, 2.3.3, 2.2.3, and 2.1.4. Organizations should ensure that they update to the patched versions to mitigate this vulnerability.
Mitigation & Remediation
Organizations should update to TensorFlow version 2.5.0, which includes the fix for this vulnerability. For those using earlier versions, it is recommended to upgrade to TensorFlow 2.4.2, 2.3.3, 2.2.3, or 2.1.4 as these versions will also receive the patch.
If immediate patching is not feasible, organizations should consider implementing configurations that limit input dimensions to prevent the exploitation of this vulnerability. Monitoring for unusual behavior in TensorFlow applications may also help detect potential exploitation attempts.
For more guidance on securing TensorFlow implementations, organizations can refer to the application security assessment services offered by AppSecure.
Detection Guidance
Organizations should monitor application logs for any errors related to the DepthwiseConv operator. Look for unusual patterns or spikes in error messages that could indicate attempts to exploit this vulnerability. Additionally, behavioral anomalies in TensorFlow applications should be investigated promptly.
AppSecure Threat Intelligence Insight
The long-term significance of CVE-2021-29602 highlights the importance of rigorous input validation in machine learning frameworks. This vulnerability serves as a reminder for security teams to prioritize secure coding practices within their development processes.
As the use of machine learning continues to rise, security teams must be vigilant in identifying and addressing potential vulnerabilities in their models and frameworks. Regular security assessments and penetration testing can aid in discovering weaknesses before they are exploited.
For organizations looking to enhance their security posture, engaging in red teaming services can provide valuable insights into potential attack vectors and vulnerabilities.
Furthermore, regular updates to libraries and frameworks, as well as conducting thorough security audits, are essential for maintaining a secure machine learning environment.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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