CVE-2021-29546 is a vulnerability in Google TensorFlow that allows an attacker to trigger an integer division by zero undefined behavior in the `tf.raw_ops.QuantizedBiasAdd` operation. This issue arises because the Eigen kernel's implementation performs a division by the number of elements of the smaller input without verifying that this value is not zero. This flaw can lead to application crashes or unexpected behavior, constituting a risk to organizations relying on TensorFlow for machine learning applications.
With a CVSS score of 2.5, this vulnerability is classified as low severity. However, organizations are advised to assess their usage of TensorFlow and the specific versions in use, as the impact could escalate depending on the application context. The vulnerability was published on May 14, 2021, and has been marked as modified, indicating that details or status may have changed since its initial disclosure.
Organizations should prioritize patching immediately, especially since the fix will be included in TensorFlow version 2.5.0, and backported to earlier supported versions including TensorFlow 2.4.2, 2.3.3, 2.2.3, and 2.1.4.
The exploitation status remains low, with no confirmed public exploits or proof-of-concept code available at this time. Organizations should remain vigilant and monitor for any updates or advisories related to this vulnerability as part of their security practices.
The urgency for defenders is moderate, reflecting the potential for impact in specific applications, but also the relatively low likelihood of widespread exploitation.
To further understand the implications of this vulnerability, organizations are encouraged to stay updated on TensorFlow's development and security patches through official channels.
Vulnerability Details
The vulnerability arises from an integer division by zero in the `tf.raw_ops.QuantizedBiasAdd` function, as detailed in the official TensorFlow documentation. The implementation of the Eigen kernel fails to check that the divisor is not zero, which can lead to undefined behavior. The CWE classification for this vulnerability is CWE-369, indicating a division by zero flaw.
The CVSS score assigned to this vulnerability varies: the primary source, NVD, assigns a score of 7.8 with high severity, while a secondary source lists a score of 2.5 with low severity. This discrepancy indicates differences in perceived impact based on the environment and usage of TensorFlow.
The affected product is TensorFlow, maintained by Google, with the vulnerability identified in multiple versions before the patch is applied. The fix is crucial for maintaining the stability and security of applications built on this platform.
Technical Analysis
The root cause of this vulnerability is the lack of validation in the division operation within the Eigen kernel. When the size of the smaller input tensor is zero, it results in a division by zero, which is undefined behavior in programming.
The attack vector for this vulnerability is local, indicating that the attacker would need access to the system where TensorFlow is running. The attack complexity is considered high, as it requires specific conditions to trigger the vulnerability successfully.
Privileges required for exploitation are low, meaning that an attacker does not need elevated permissions to trigger the vulnerability. User interaction is not required, which increases the risk of exploitation.
In terms of impact, this vulnerability does not affect confidentiality or integrity but can impact availability, as it may lead to application crashes.
Risk & Impact Analysis
The real-world deployment risk associated with CVE-2021-29546 primarily revolves around applications relying heavily on TensorFlow for critical functions. If exploited, this vulnerability could lead to application failures, impacting business operations.
The blast radius of this vulnerability is limited to applications using the affected TensorFlow versions, but organizations should consider the potential for cascading failures in interconnected systems.
Given the CVSS score of 7.8 from the NVD, the urgency for organizations to address this vulnerability is high, as it indicates significant risks that can be mitigated by timely patching.
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 2.1.4, and from version 2.2.0 to 2.2.3, 2.3.0 to 2.3.3, and 2.4.0 to 2.4.2. Organizations using these versions should ensure they update to the latest patched versions to mitigate the risk.
Mitigation & Remediation
Organizations are encouraged to apply the patch included in TensorFlow 2.5.0 and to upgrade to this version or any later version available. For those unable to upgrade immediately, implementing configuration hardening and monitoring system behavior can help mitigate risks until the patch is applied.
Additionally, organizations should consider conducting a thorough review of their TensorFlow configurations and usage patterns, especially in production environments, to identify any potential vulnerabilities and ensure compliance with security best practices.
Application security assessment services can also help organizations identify weaknesses and improve their security posture.
Detection Guidance
Organizations should monitor logs for any indications of abnormal behavior related to TensorFlow operations. Behavioral anomalies, particularly related to tensor operations, may indicate attempts to exploit this vulnerability.
Network signatures that detect unusual patterns in TensorFlow traffic can also provide indicators of potential exploitation attempts. System changes should be closely monitored, especially updates that coincide with TensorFlow updates.
AppSecure Threat Intelligence Insight
The long-term significance of CVE-2021-29546 lies in its representation of the broader risks associated with machine learning frameworks. As these tools become increasingly integrated into critical systems, vulnerabilities such as this one can have amplified effects.
This vulnerability reflects a common pattern where insufficient input validation can lead to severe operational impacts. Security teams should take this as a strategic defensive takeaway, ensuring that rigorous input validations are in place across all components of machine learning systems.
For more information on improving application security and preventing similar vulnerabilities, organizations can refer to resources like our penetration testing methodology or consider leveraging our penetration testing services to identify and mitigate vulnerabilities effectively.
Regular updates, combined with proactive security measures, will help organizations safeguard their TensorFlow implementations against evolving threats.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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