CVE-2022-23586 is a medium-severity vulnerability affecting Google TensorFlow, an open-source machine learning framework. This vulnerability allows a malicious user to cause a denial of service by altering a `SavedModel`, leading to falsified assertions in `function.cc` that can crash the Python interpreter. The CVSS score for this vulnerability is 6.5, indicating a medium level of severity. It is crucial for organizations using TensorFlow to understand the potential risk and impact associated with this vulnerability.
The vulnerability was published on February 4, 2022, and has since been modified. It is important for organizations to act quickly, as the availability impact is rated high, which means that systems may be rendered unavailable due to exploitation of this vulnerability. Organizations should prioritize patching immediately to mitigate risks.
The fix for this vulnerability will be included in TensorFlow version 2.8.0, and it will also be cherry-picked for earlier versions: TensorFlow 2.7.1, 2.6.3, and 2.5.3. It is essential for users to update their installations to these versions to ensure their systems are protected.
Currently, there is no known public exploit for this vulnerability, but the nature of denial-of-service vulnerabilities requires immediate attention. Organizations should assess their exposure and implement the necessary updates to prevent possible service disruptions.
In summary, CVE-2022-23586 poses a medium risk to users of TensorFlow, and it is crucial that organizations take proactive measures to patch their systems against this vulnerability.
Vulnerability Details
The vulnerability allows a malicious user to alter a `SavedModel` in such a way that assertions in `function.cc` are falsified, leading to a crash of the Python interpreter. The issue is classified under CWE-617, indicating a direct impact on availability. The vulnerability affects all versions of TensorFlow prior to 2.8.0, including 2.5.2 and versions from 2.6.0 to 2.6.2, as well as 2.7.0.
Technical Analysis
This vulnerability's root cause lies in the way TensorFlow handles `SavedModel` objects. A malicious user can manipulate these objects to falsify assertions, leading to a denial of service. The attack vector is through the network, with a low attack complexity requiring low privileges and no user interaction. The availability impact is rated high, indicating a significant risk of service disruption.
Risk & Impact Analysis
Risk to organizations includes potential denial of service, which could impact operations and availability of services relying on TensorFlow. Given the high availability impact and the ease of exploitation, this vulnerability should be addressed in the priority patch cycle. Organizations are encouraged to assess their systems and take action to mitigate risks.
Exploitation Status
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
All versions of TensorFlow prior to 2.8.0 are affected, including 2.5.2, 2.6.0 to 2.6.2, and 2.7.0. Organizations should ensure they upgrade to the patched versions to mitigate this vulnerability.
Mitigation & Remediation
Organizations should patch their TensorFlow installations to the latest version, 2.8.0, to eliminate this vulnerability. For those using earlier versions, updates to TensorFlow 2.7.1, 2.6.3, or 2.5.3 are also recommended. Implementing robust security practices and maintaining updated software can further enhance security. Organizations can validate remediation effectiveness through penetration testing to ensure that similar weaknesses are addressed.
Detection Guidance
Monitoring logs for unusual behavior, especially around the use of `SavedModel` in TensorFlow, can provide indications of potential exploitation attempts. Organizations should be aware of behavioral anomalies that could indicate attempts to falsify assertions in the framework.
AppSecure Threat Intelligence Insight
The long-term significance of CVE-2022-23586 lies in its demonstration of how vulnerabilities in widely-used frameworks like TensorFlow can lead to substantial operational impact. This incident highlights the importance of continuous monitoring and updating of software dependencies. Security teams should consider implementing regular security assessments, such as vulnerability management programs to address such vulnerabilities proactively. Additionally, the pattern of exploitation through denial of service emphasizes the need for rigorous testing and validation of all software components. Organizations should also consider adopting penetration testing methodologies to uncover potential vulnerabilities before they can be exploited.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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