CVE-2021-29587 is a vulnerability identified in Google TensorFlow, an end-to-end open-source platform for machine learning. The vulnerability arises from the `Prepare` step of the `SpaceToDepth` TFLite operator, which does not adequately check for a zero value before division. An attacker could craft a model where `params->block_size` equals zero, leading to potential application instability. This vulnerability has been officially classified as low severity with a CVSS score of 2.5.
The potential impact of this vulnerability is limited due to its low severity, but organizations using affected versions of TensorFlow should remain vigilant. The vulnerability was published on May 14, 2021, and has since been modified, indicating ongoing attention from the security community. The vulnerability is classified under CWE-369, which refers to division by zero issues.
Organizations should prioritize patching this vulnerability in their TensorFlow deployments, as a fix is included in TensorFlow version 2.5.0. Additionally, the commit will be cherry-picked for earlier supported versions, including TensorFlow 2.4.2, 2.3.3, 2.2.3, and 2.1.4. Monitoring for this vulnerability and implementing the patch is essential to safeguard against any potential exploitation.
As of now, there are no publicly available exploits for this vulnerability, and it is not included in the Known Exploited Vulnerabilities (KEV) catalog. Organizations should still approach this issue with caution and ensure that they remain updated on the latest patches and security advisories.
Given the low CVSS score, the urgency for remediation is classified as low. However, it is advisable for organizations to schedule updates in their routine maintenance.
CVE-2021-29587 serves as a reminder of the importance of rigorous validation processes in software development, particularly in open-source platforms like TensorFlow. Organizations should continually evaluate their security practices to mitigate risks associated with such vulnerabilities.
Vulnerability Details
The CVE description highlights the vulnerability's nature and its potential impact. The vulnerability type is classified as a local privilege escalation due to the division by zero scenario that can occur in the TFLite operator. The CVSS score assigned is 2.5, categorized under low severity, indicating a low level of urgency for organizations to act.
Technical Analysis
The root cause of CVE-2021-29587 lies in the absence of an appropriate validation check for the `params->block_size` variable before performing a division operation. This oversight allows attackers to craft malicious models that could exploit this flaw, leading to application instability.
The attack vector for this vulnerability is classified as local, requiring low privileges for exploitation. The attack complexity is rated as high, meaning that specific conditions or knowledge about the system configuration may be necessary for successful exploitation. User interaction is not required, and the impact on confidentiality and integrity is rated as none, with a low impact on availability.
Risk & Impact Analysis
Risk to organizations includes potential instability of applications utilizing TensorFlow, which could lead to service interruptions or degraded performance. While the overall impact is low, the blast radius is contingent on the deployment scale and usage of affected TensorFlow versions. Given the low CVSS score, organizations may choose to address this vulnerability in their routine maintenance schedule rather than as an immediate priority.
The low exploitability score suggests that while the vulnerability exists, the likelihood of it being exploited in the wild is currently low. Nevertheless, organizations should not overlook the importance of maintaining up-to-date versions of software components to mitigate even low-severity vulnerabilities.
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, as well as versions 2.2.0 through 2.2.3, 2.3.0 through 2.3.3, and 2.4.0 through 2.4.2. Users are advised to upgrade to TensorFlow 2.5.0 or the latest version to mitigate this vulnerability.
Mitigation & Remediation
Organizations should prioritize patching TensorFlow installations to version 2.5.0, where the vulnerability has been resolved. If immediate upgrading is not feasible, organizations should consider implementing workarounds that involve avoiding models that may induce a division by zero.
For those unable to update immediately, considering configuration hardening and establishing network controls to monitor and limit the use of TensorFlow in production environments can help mitigate risk while waiting for a patch.
Organizations may also benefit from continuous security testing to validate that the deployed version of TensorFlow is not vulnerable to known issues. Regular assessments can help identify any potential threats and implement timely remediation.
Detection Guidance
To detect potential exploitation attempts or any anomalies related to this vulnerability, organizations should monitor application logs for unusual error messages or crashes that may indicate a division by zero. Behavioral anomalies in model performance should also be investigated.
Network signatures can be established to identify any unauthorized access attempts targeting TensorFlow deployments. Additionally, understanding system changes that may indicate an exploit is critical for early detection.
AppSecure Threat Intelligence Insight
CVE-2021-29587 represents a significant reminder of the importance of secure coding practices, particularly in open-source software development. The lack of input validation can lead to vulnerabilities that disrupt operations and compromise security. Organizations should take this opportunity to review their development practices and ensure proper validation checks are in place.
This vulnerability also highlights a trend in the software development lifecycle where even low-severity vulnerabilities can have cascading effects if left unaddressed. As such, teams should prioritize the regular auditing of their software components and dependencies.
In summary, organizations should not only remediate CVE-2021-29587 but also use it as a catalyst for improving their overall security posture. Engaging in rigorous security assessments, such as penetration testing, can help identify similar weaknesses and fortify defenses against future vulnerabilities.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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