CVE-2021-29557 is a vulnerability in Google TensorFlow, an end-to-end open-source platform for machine learning. This vulnerability allows attackers to cause a denial of service via a floating-point exception (FPE) runtime error in `tf.raw_ops.SparseMatMul`. The issue arises from a division by zero that occurs deep in the Eigen code when the `b` tensor is empty. The fix for this vulnerability is included in TensorFlow version 2.5.0, with additional backports to TensorFlow versions 2.4.2, 2.3.3, 2.2.3, and 2.1.4, which are also affected and still within the supported range.
The severity level of this vulnerability is classified as low, with a CVSS base score of 2.5. Despite its lower severity, organizations should be aware of the risk to their systems, particularly in environments where TensorFlow is deployed. The availability impact is rated as low, indicating that while the vulnerability may not lead to immediate system compromise, it can still disrupt service, warranting prompt attention from security teams.
At present, there are no known exploits publicly available for CVE-2021-29557, and it is not listed in the Known Exploited Vulnerabilities (KEV) catalog. However, organizations should not become complacent. Regular updates and patching are essential to maintaining security, particularly in technology stacks that utilize TensorFlow.
Organizations should prioritize patching immediately to mitigate any potential risks associated with this vulnerability. Ensuring that all systems are updated to the latest version of TensorFlow will significantly reduce the attack surface and help maintain operational integrity.
Vulnerability Details
The official CVE description indicates that this vulnerability allows a denial of service through a FPE error in `tf.raw_ops.SparseMatMul`. The root cause is a division by zero within Eigen code due to an empty tensor. The vulnerability affects TensorFlow versions prior to 2.5.0, with the fix slated for inclusion in this release and backporting to earlier versions still under support.
The CWE classification for this vulnerability is CWE-369, which pertains to division by zero errors. The CVSS score varies between different sources: it is rated as 2.5 by GitHub and 5.5 by NVD, indicating a low to medium severity based on the context of impact and exploitability.
The vulnerability is classified as having a low attack complexity and requires low privileges, which means that an attacker with minimal access could potentially exploit this issue.
Technical Analysis
The root cause of CVE-2021-29557 lies in the execution of `tf.raw_ops.SparseMatMul`, where a division by zero can occur if the `b` tensor is empty. This results in a floating-point exception that can lead to a denial of service. The attack vector is classified as local, meaning the exploit must be executed in an environment where TensorFlow is running.
The attack complexity is rated as high, indicating that successful exploitation may require specific conditions to be met. Privileges required for exploitation are low, and no user interaction is necessary, which raises the potential for exploitation.
In terms of impact, the availability impact is rated as low by GitHub, while NVD rates it as high, indicating a disparity in how the impact of this vulnerability is perceived. The lack of confidentiality and integrity impact further emphasizes the nature of the vulnerability as primarily affecting service availability.
Risk & Impact Analysis
Risk to organizations includes potential denial of service scenarios that could disrupt operations, particularly for applications reliant on TensorFlow for machine learning tasks. The availability impact is significant enough to warrant attention, especially in production environments where uptime is critical.
Organizations should assess their usage of TensorFlow and prioritize patching as part of their security protocols. The urgency for remediation should align with the organization's patch management policy, considering the low severity of the vulnerability but the potential operational impact.
Given the nature of the vulnerability, organizations may schedule remediation within their routine maintenance cycles but should remain vigilant for any signs of exploitation or issues arising from the vulnerability.
Exploitation Status
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
The vulnerability affects all versions of TensorFlow prior to 2.5.0, specifically versions 2.1.4 and 2.4.0 through 2.4.2, as well as 2.2.0 through 2.2.3, and 2.3.0 through 2.3.3. Organizations should ensure they are using a version that includes the fix.
Mitigation & Remediation
Organizations should update to TensorFlow version 2.5.0 or later to mitigate this vulnerability. If immediate upgrading is not possible, consider implementing configuration hardening to limit the impact of potential exploitation.
For guidance on penetration testing and validating the effectiveness of mitigations, organizations can refer to penetration testing methodology to identify any remaining vulnerabilities in their environment.
Detection Guidance
Organizations should monitor logs for any anomalies related to the use of TensorFlow, particularly during operations involving `SparseMatMul`. Look for unusual patterns or failures that may indicate attempts to exploit this vulnerability.
AppSecure Threat Intelligence Insight
The long-term significance of CVE-2021-29557 lies in its representation of how even low-severity vulnerabilities can have operational impacts in critical systems. It underscores the importance of maintaining robust patch management processes.
Organizations should analyze patterns of vulnerability exposure in their technology stacks, and ensure they are prepared to address similar vulnerabilities in the future. For comprehensive vulnerability management, teams may consider implementing a vulnerability management program that aligns with industry best practices.
In conclusion, CVE-2021-29557 serves as a reminder for security teams to stay vigilant regarding vulnerabilities in widely-used libraries like TensorFlow. Adopting a proactive approach to vulnerability assessments can help minimize risk exposure and enhance overall security posture.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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