CVE-2021-29554 is a low-severity vulnerability affecting Google TensorFlow, an open-source platform widely used for machine learning. This vulnerability allows attackers to cause a denial of service due to a floating-point exception (FPE) runtime error in `tf.raw_ops.DenseCountSparseOutput`. The error arises because the divisor computed from user data can be zero, leading to a crash when division is attempted. The vulnerability is particularly concerning as it can be exploited without requiring high privileges or user interaction.
The CVSS score for this vulnerability is 2.5, classified as low severity. The attack vector is local, and the complexity is rated as high, meaning that exploitation is not straightforward. However, the potential availability impact is categorized as low, indicating that while the service may be disrupted, it does not lead to significant data loss or unauthorized access.
Organizations using TensorFlow should prioritize patching this vulnerability, particularly if they operate in environments where TensorFlow is utilized for critical applications. The fix is included in TensorFlow version 2.5.0, with backports available for earlier versions, specifically TensorFlow 2.4.2 and 2.3.3.
The urgency for defenders is moderate; while the attack complexity is high, organizations should remain vigilant about potential denial of service attacks stemming from this issue.
Vulnerability Details
The official description of CVE-2021-29554 states that the vulnerability is due to the implementation of the `tf.raw_ops.DenseCountSparseOutput` operation. The divisor is calculated based on user data, but there is no validation to ensure that the divisor is not zero before performing the division. This flaw can lead to a denial of service if an attacker can manipulate the input values.
The known CVSS scores are as follows:
Source | CVSS Score |
|---|---|
NVD | 5.5 |
GitHub | 2.5 |
Technical Analysis
The root cause of this vulnerability lies in the improper handling of user input within the TensorFlow codebase. Specifically, the implementation computes a divisor value from the `values` argument without validating that the computed result is non-zero. This lack of validation allows attackers to exploit the operation by providing input that results in a zero divisor, ultimately leading to a runtime error and potential denial of service.
The attack vector is local, meaning that an attacker must have access to the system executing TensorFlow to exploit this vulnerability. The attack complexity is rated as high, indicating that exploiting this vulnerability requires an understanding of TensorFlow's operations and how to manipulate the input values to trigger the fault.
Privileges required for exploitation are low, as the attacker does not need elevated privileges to trigger the vulnerability. Importantly, user interaction is not required, making this a more concerning vulnerability as it can be triggered without any action from the user.
Risk & Impact Analysis
Risk to organizations includes the potential for denial of service, which could impact services relying on TensorFlow for machine learning tasks. Organizations should consider the blast radius of this vulnerability, particularly if TensorFlow is integrated into critical systems or applications. The availability impact is classified as low, but the consequences of service disruption should still prompt organizations to address this vulnerability.
The CVSS score of 5.5 indicates a moderate concern, and although not classified as high severity, organizations should understand that the combination of low privileges required and local attack vector could lead to operational disruptions.
Organizations should address this vulnerability in their patching cycles, as the fix is already available in TensorFlow versions 2.5.0 and later. Those still using older versions should prioritize upgrading to mitigate potential risks.
Exploitation Status
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
Affected versions include TensorFlow versions prior to 2.1.4, versions 2.2.0 to 2.2.2, versions 2.3.0 to 2.3.2, and versions 2.4.0 to 2.4.1. Users of TensorFlow should ensure they are updated to version 2.5.0 or later to mitigate this vulnerability.
Mitigation & Remediation
To remediate this vulnerability, organizations should upgrade to TensorFlow version 2.5.0 or later, where the fix has been implemented. If immediate upgrading is not feasible, organizations can consider implementing workarounds such as input validation to prevent zero divisors in operations.
For ongoing security posture, organizations are encouraged to engage in penetration testing to identify potential vulnerabilities in their environment.
Detection Guidance
Organizations should monitor logs for anomalies associated with TensorFlow operations, particularly those involving the `tf.raw_ops.DenseCountSparseOutput` operation. Additionally, security teams should look for behavioral indicators that suggest exploitation attempts, such as frequent crashes or errors related to division operations within TensorFlow.
AppSecure Threat Intelligence Insight
The long-term significance of CVE-2021-29554 highlights the importance of rigorous input validation in software development, particularly in libraries that handle user data. This vulnerability represents a trend where complex machine learning frameworks can unintentionally expose themselves to denial of service through unhandled edge cases.
Security teams must prioritize thorough testing and vulnerability assessments in their development processes to mitigate such risks. Engaging in penetration testing methodologies can provide insights into potential weaknesses before they can be exploited.
Additionally, organizations should consider integrating security practices into their development lifecycle to catch vulnerabilities early. Regular updates and patches are crucial in maintaining a secure environment, especially for widely used libraries such as TensorFlow.
For comprehensive assessment and security validation, consider utilizing AppSecure's application security assessment services to enhance your 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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