The vulnerability identified as CVE-2022-21733 pertains to Google TensorFlow, an Open Source Machine Learning Framework. This vulnerability allows a denial of service attack due to an out of memory condition that can be triggered by an integer overflow during the implementation of `StringNGrams`. The lack of validation on `pad_width` results in the computation of a negative value for `ngram_width`, which is used later for memory allocation. As a result, this can lead to unexpected behavior in the application.
The severity of this vulnerability is classified as medium, with a CVSS score of 4.3. This score indicates a moderate risk level, primarily due to the potential for denial of service. The vulnerability affects versions of TensorFlow prior to 2.6.3, with the fix included in TensorFlow 2.8.0. The vendor has committed to cherrypicking this fix for earlier versions still in the supported range.
Risk to organizations includes potential service disruptions caused by denial of service attacks, which can affect the availability of applications relying on TensorFlow. Organizations should prioritize patching this vulnerability promptly to mitigate the associated risks and maintain service continuity.
Currently, there are no known exploits in the wild for this vulnerability, and it is not listed in the Known Exploited Vulnerabilities (KEV) database. However, organizations should remain vigilant and apply the necessary patches as soon as they are available to prevent any future exploitation.
Organizations should address this vulnerability in their priority patch cycle to ensure protection against potential denial of service conditions.
Vulnerability Details
CVE-2022-21733 is characterized by an integer overflow issue in TensorFlow's `StringNGrams` implementation. The lack of validation on `pad_width` allows for the calculation of a negative `ngram_width`, which subsequently leads to memory allocation failures. The vulnerability has been assigned a CVSS 3.1 score of 4.3 and is classified as medium severity.
The vulnerability affects the following versions of TensorFlow: 2.5.2 and all versions between 2.6.0 to 2.6.2, as well as 2.7.0. The official fix will be included in TensorFlow version 2.8.0, with cherrypicks planned for the affected earlier versions.
Technical Analysis
The root cause of CVE-2022-21733 lies in the absence of validation checks for `pad_width`, leading to an integer overflow that computes a negative value for `ngram_width`. This critical oversight results in a denial of service vulnerability due to memory allocation errors when TensorFlow attempts to allocate negative memory.
The attack vector for this vulnerability is network-based, meaning an attacker can exploit it remotely without needing physical access or user interaction. The attack complexity is classified as low, as it requires minimal skills and knowledge to trigger the vulnerability. Privileges required for exploitation are low, suggesting that an unauthenticated attacker could potentially exploit this vulnerability.
In terms of impact, this vulnerability primarily affects the availability of the system, with a low impact on confidentiality and integrity. Successful exploitation can lead to service disruptions, making it imperative for organizations to address this issue proactively.
Risk & Impact Analysis
The real-world implications of CVE-2022-21733 are significant, particularly for organizations utilizing TensorFlow in production environments. The potential for denial of service can disrupt critical machine learning services, impacting operations and user experience. The scope for such disruptions is broad, affecting any application relying on TensorFlow for processing data.
Given the medium severity of this vulnerability, organizations should assess its urgency based on their deployment of TensorFlow. Those running affected versions should prioritize applying the patch to minimize downtime and maintain service integrity.
Organizations should schedule remediation activities as part of their vulnerability management program, ensuring that this issue is addressed in upcoming patch cycles.
Exploitation Status
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
The following versions of TensorFlow are affected by CVE-2022-21733: TensorFlow 2.5.2, TensorFlow 2.6.0 to 2.6.2, and TensorFlow 2.7.0. Organizations using these versions should apply the necessary patches.
Mitigation & Remediation
Organizations should prioritize patching this vulnerability by updating to TensorFlow version 2.8.0 or later. For those using earlier versions, the relevant patches have been cherrypicked for TensorFlow versions 2.7.1, 2.6.3, and 2.5.3. It is critical to ensure that all affected systems are updated promptly.
In the absence of immediate patch availability, organizations may consider implementing configuration hardening measures and network controls to limit exposure to potential denial of service attacks. Regular monitoring for unusual system behavior can also help detect any exploitation attempts.
Detection Guidance
To detect potential exploitation of this vulnerability, organizations should monitor system logs for unusual memory allocation patterns or service disruptions. Behavioral anomalies during TensorFlow operations may also indicate attempts to exploit this vulnerability. Implementing network signatures to identify exploit attempts can further enhance detection capabilities.
AppSecure Threat Intelligence Insight
The long-term significance of CVE-2022-21733 lies in its representation of common vulnerabilities found in machine learning frameworks. As the usage of such frameworks continues to grow, ensuring robust validation and error handling in their implementations is paramount to prevent denial of service and other potential attacks.
Security teams should take this incident as a lesson to conduct thorough security assessments of their ML frameworks. Adopting a proactive security stance can help mitigate risks associated with similar vulnerabilities in the future.
For organizations looking to bolster their security posture, investing in continuous penetration testing services can provide ongoing assessments and help identify vulnerabilities in real-time.continuous penetration testing is an effective strategy for maintaining the security of machine learning frameworks 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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