CVE-2021-29596 is a vulnerability associated with the Google TensorFlow platform, specifically affecting the implementation of the `EmbeddingLookup` TFLite operator. This vulnerability allows for a potential division by zero error if an attacker crafts a model where the first dimension of the `value` input is set to zero. Given TensorFlow's widespread use in machine learning applications, this issue could have significant implications for organizations relying on this technology. The vulnerability has a CVSS score of 2.5, categorizing it as low severity.
As the vulnerability was published on May 14, 2021, organizations should be aware that fixes will be included in TensorFlow version 2.5.0. Additionally, it will be cherry-picked into earlier supported versions, specifically TensorFlow 2.4.2, 2.3.3, 2.2.3, and 2.1.4. The urgency for patching is moderated, given the low severity; however, organizations are encouraged to mitigate risks by upgrading.
The implications of this vulnerability are primarily associated with availability, as a successful exploit could lead to application crashes or degraded performance. While the confidentiality and integrity impacts are assessed as none, ensuring that systems are patched is essential to maintain operational stability.
As of now, there are no public exploits known to be available for this vulnerability, nor is it currently included in the Known Exploited Vulnerabilities (KEV) database. Therefore, organizations should address this vulnerability in their routine security maintenance cycle.
Organizations should prioritize patching immediately. Failure to do so may expose systems to unnecessary risks, even if the immediate threat level remains low.
Vulnerability Details
The vulnerability in TensorFlow allows for a division by zero error in the `EmbeddingLookup` TFLite operator. The CVSS score assigned to this vulnerability is 7.8, indicating a high severity when viewed under different assessment parameters. The vulnerability's CWE classification is CWE-369, representing an arithmetic operation that is improperly validated. This underscores the importance of rigorous input validation in machine learning frameworks.
Technical Analysis
The root cause of CVE-2021-29596 stems from insufficient validation in the `EmbeddingLookup` operator's implementation, specifically when processing the dimensions of the input data. The attack vector is classified as local, requiring an attacker to have access to the environment where TensorFlow is executed. The attack complexity is rated high, meaning that a successful exploit is not trivial and requires specific conditions to be met.
In this context, low privileges are required by the attacker, and no user interaction is necessary for the exploit to succeed. The impact on availability is classified as low, indicating that while the exploit may not lead to a full system compromise, it could still disrupt the normal operation of the application.
Risk & Impact Analysis
Risk to organizations includes potential application crashes and degraded performance, particularly in environments where TensorFlow is integral to operations. While the confidentiality and integrity of data are not directly impacted, the availability issues could lead to business interruptions. Organizations using TensorFlow in production should monitor their systems closely and implement the necessary patches as soon as they are available.
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 2.5.0, specifically TensorFlow versions 2.1.4, 2.2.0 up to (but not including) 2.2.3, 2.3.0 up to (but not including) 2.3.3, and 2.4.0 up to (but not including) 2.4.2. Organizations should ensure they are using the latest version to mitigate this vulnerability.
Mitigation & Remediation
To mitigate this vulnerability, organizations should upgrade to TensorFlow version 2.5.0 or later. If immediate upgrading is not feasible, organizations are advised to apply the patch that will also be available for TensorFlow versions 2.4.2, 2.3.3, 2.2.3, and 2.1.4. Additionally, organizations should consider implementing robust input validation mechanisms in their models to prevent similar vulnerabilities in the future.
For further details on securing TensorFlow applications, organizations may refer to the application security assessment guide provided by AppSecure.
Detection Guidance
Organizations should monitor for unusual application behaviors, particularly those involving TensorFlow operations. Log indicators should include errors related to input validation failures or unexpected dimensions in model inputs. Behavioral anomalies may indicate attempts to exploit this vulnerability.
AppSecure Threat Intelligence Insight
The potential for exploitation in low-severity vulnerabilities such as CVE-2021-29596 should not be underestimated. Although it is not currently exploited in the wild, the existence of such vulnerabilities can lead to security complacency. Security teams should adopt a proactive approach to vulnerability management, ensuring that all components are regularly reviewed and patched. This can significantly reduce the risk of systemic failures.
For more insights on vulnerability management strategies, organizations can refer to the vulnerability management program article.
Additionally, continuous assessment of application security can be fortified through penetration testing, allowing organizations to identify potential vulnerabilities before they are exploited.
In conclusion, CVE-2021-29596 highlights the necessity for ongoing vigilance and proactive measures in software security. Organizations must prioritize timely updates and comprehensive security assessments.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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