CVE-2022-29211 is a medium-severity vulnerability found in Google TensorFlow, specifically in the implementation of `tf.histogram_fixed_width`. This vulnerability allows the application to crash when the values array contains `Not a Number` (NaN) elements. The issue arises because the function assumes all floating point operations are defined, leading to a crash when NaN is encountered. This vulnerability affects versions prior to 2.9.0, including 2.8.1, 2.7.2, and 2.6.4. Organizations using these versions should prioritize patching immediately.
The CVSS score for this vulnerability is 5.5, classifying it as medium severity. Affected systems may experience degradation of availability due to potential crashes, which can interrupt machine learning workflows. The impact on confidentiality and integrity is minimal, as the vulnerability does not expose sensitive data or allow unauthorized modifications.
With the current state of exploitation, there are no known public exploits available, and the vulnerability is not listed in the Known Exploited Vulnerabilities (KEV) database. However, organizations should remain vigilant and monitor their systems, as local attack vectors with low complexity could be leveraged if the vulnerability is not addressed.
Organizations using TensorFlow should upgrade to versions 2.9.0, 2.8.1, 2.7.2, or 2.6.4, which contain patches for this vulnerability. This proactive measure is essential to ensure the stability and security of applications relying on TensorFlow.
Vulnerability Details
The vulnerability in TensorFlow is classified under CWE-20, indicating improper input validation. The crash occurs due to a failure in handling NaN values during floating point operations, leading to a division by NaN and subsequent casting to int32, which causes the crash.
The specific CVSS vector for this vulnerability is CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H, where the availability impact is rated as high. This indicates that the exploit can cause significant downtime for applications.
Technical Analysis
The root cause of CVE-2022-29211 lies in the assumption made by TensorFlow regarding the values passed into the `tf.histogram_fixed_width` function. When values include NaN, the calculation becomes undefined, resulting in a crash.
The attack vector is local, requiring low privileges and no user interaction. This local nature implies that an attacker would need access to the system running TensorFlow to exploit this vulnerability.
The complexity of the attack is classified as low, meaning that an attacker could potentially exploit this vulnerability without significant effort. The impacts on confidentiality and integrity are none, while availability is rated high due to the application crashing.
Risk & Impact Analysis
Risk to organizations includes potential downtime of TensorFlow applications, which can hinder machine learning processes and lead to operational inefficiencies. Given the reliance on TensorFlow in numerous applications, the blast radius of this vulnerability can be significant, affecting any service utilizing TensorFlow that processes numerical data.
Organizations should assess their deployment of TensorFlow and prioritize the patching of affected versions. The urgency for remediation is classified as medium, given the availability impact and the potential for crashes. Regular updates and monitoring of systems using TensorFlow will help mitigate risks associated with this vulnerability.
Exploitation Status
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
The affected versions of TensorFlow are all versions prior to 2.9.0, specifically including 2.8.1, 2.7.2, and 2.6.4. Organizations should ensure they are running patched versions to avoid the risk of crashes.
Mitigation & Remediation
Organizations should upgrade to TensorFlow versions 2.9.0, 2.8.1, 2.7.2, or 2.6.4 to mitigate this vulnerability. For those unable to immediately apply patches, consider implementing workarounds such as input validation to check for NaN values before invoking `tf.histogram_fixed_width`. Additionally, maintaining strict monitoring of TensorFlow applications can help detect unusual behavior indicative of exploitation attempts.
For further security assessments, organizations can utilize application security assessments to enhance their defenses.
Detection Guidance
Monitor logs for entries that indicate unexpected crashes or segmentation faults related to TensorFlow applications. Behavioral anomalies, such as sudden drops in application availability or performance metrics, should also be investigated. Implement network intrusion detection systems to catch potential exploitation attempts.
AppSecure Threat Intelligence Insight
The long-term significance of CVE-2022-29211 lies in its potential to affect critical machine learning infrastructures. As organizations increasingly rely on TensorFlow for their applications, vulnerabilities of this nature highlight the need for proactive security measures. This incident exemplifies the importance of vulnerability management and regular updates to software components.
Security teams should take this opportunity to review their vulnerability management programs and ensure that they are equipped to handle similar vulnerabilities in the future.
In conclusion, keeping abreast of security patches and understanding the implications of vulnerabilities like CVE-2022-29211 can greatly enhance an organization's resilience against potential threats. Regular training and awareness programs for development teams can also mitigate the risks associated with the introduction of vulnerabilities in code.
For comprehensive insights on security testing, organizations can explore penetration testing methodologies to strengthen their security posture.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
.webp)