CVE-2022-35993 is a medium-severity vulnerability affecting Google TensorFlow, an open-source platform for machine learning. The issue arises when the `SetSize` operation receives an input `set_shape` that is not a 1D tensor, resulting in a `CHECK` failure. This flaw can potentially be exploited to trigger a denial of service (DoS) attack. The vulnerability is significant as it could lead to service interruptions for applications relying on TensorFlow.
The CVSS score for this vulnerability is 5.9, indicating a medium severity level. The attack vector is classified as network-based, with a high attack complexity and no privileges or user interactions required for exploitation. As such, this vulnerability poses a notable risk to organizations utilizing TensorFlow, particularly those with network-accessible systems.
Organizations should prioritize patching immediately, as the fix for this vulnerability is included in TensorFlow version 2.10.0. Additionally, the patch has been backported to TensorFlow versions 2.9.1, 2.8.1, and 2.7.2, which remain supported. No known workarounds are available, making it essential for users to update to the patched versions.
Given the potential for service disruption due to this vulnerability, it is critical for organizations to assess their exposure and implement the necessary updates to mitigate risks associated with CVE-2022-35993.
Vulnerability Details
The vulnerability allows for denial of service attacks when TensorFlow’s `SetSize` function is invoked with an invalid input shape. The issue was patched in GitHub commit cf70b79d2662c0d3c6af74583641e345fc939467, and this fix will be included in TensorFlow version 2.10.0. The affected versions include TensorFlow 2.7.2 and all versions prior to 2.10.0, as well as versions 2.8.0 and 2.9.0.
The official CVSS score of 5.9 classifies this vulnerability as medium severity. The attack vector is network-based, requiring no privileges or user interaction, which increases the risk of exploitation. The vulnerability is classified under CWE-617 for improper input validation.
Technical Analysis
The root cause of this vulnerability is related to the handling of input shapes in the `SetSize` operation. Specifically, the function fails to validate that the provided input is a 1D tensor, leading to a crash when it receives a different shape. The attack vector for this vulnerability is through the network, where an attacker can send crafted requests to an exposed TensorFlow service.
The attack complexity is high, as an attacker must construct a specific input that triggers the failure. However, no user interaction is required to exploit this vulnerability. The impact on availability is high, as successful exploitation can result in service downtime.
Risk & Impact Analysis
Organizations using TensorFlow should assess their deployments for exposure to CVE-2022-35993. The risk includes potential service disruptions that could affect application availability and user experience. With a CVSS score of 5.9, this vulnerability represents a medium risk, but given the nature of denial of service attacks, the impact could be significant depending on the application's reliance on TensorFlow.
The urgency of addressing this vulnerability should be high, as the impact on availability could lead to loss of revenue and trust from users. Organizations are encouraged to schedule remediation as soon as possible, particularly for services that are critical to their operations.
Exploitation Status
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
The affected product is TensorFlow. Specifically, all versions prior to 2.10.0 are vulnerable, including 2.8.0 and 2.9.0. The versions 2.7.2 and earlier are also impacted.
Mitigation & Remediation
To mitigate the risk associated with CVE-2022-35993, organizations should update their TensorFlow installations to version 2.10.0 or later. For those using versions 2.9.1, 2.8.1, or 2.7.2, it is critical to apply the necessary security patches that address this vulnerability.
If immediate patching is not possible, organizations should consider implementing network controls to limit access to TensorFlow services, thereby reducing the risk of exploitation until the patch can be applied.
For ongoing security assurance, organizations may benefit from integrating continuous penetration testing services into their security strategy to proactively identify and address vulnerabilities.
Detection Guidance
Organizations should monitor logs for any unusual activity related to TensorFlow operations, especially those involving the `SetSize` function. Behavioral anomalies, such as unexpected service crashes or performance degradation, can indicate attempts to exploit this vulnerability.
Network signatures that detect malformed requests to TensorFlow endpoints may also assist in identifying potential exploitation attempts.
AppSecure Threat Intelligence Insight
CVE-2022-35993 highlights the ongoing challenges in maintaining secure coding practices, especially in complex software like machine learning frameworks. This vulnerability underscores the importance of rigorous input validation to prevent denial of service issues.
As organizations increasingly adopt AI and machine learning, the security implications of such vulnerabilities become more pronounced. Regular security assessments, including penetration testing, are essential to identify weaknesses before they can be exploited by attackers.
For comprehensive security strategies, organizations should prioritize continuous monitoring and proactive vulnerability management, ensuring that systems remain resilient against evolving threats.
penetration testing can help identify similar vulnerabilities in other systems and applications.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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