TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, there is a potential for segfault / denial of service in TensorFlow by calling `tf.compat.v1.*` ops which don't yet have support for quantized types, which was added after migration to TensorFlow 2.x. In these scenarios, since the kernel is missing, a `nullptr` value is passed to `ParseDimensionValue` for the `py_value` argument. Then, this is dereferenced, resulting in segfault. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.
The CVSS score for this vulnerability is 5.5, categorized as medium severity. The impact includes a high availability risk, meaning that attackers may leverage this vulnerability to cause denial of service conditions within affected systems.
Organizations should prioritize patching immediately. The identified versions with vulnerabilities include TensorFlow releases prior to 2.9.0, specifically 2.8.1, 2.7.2, and 2.6.4, which have been patched.
As this vulnerability has been published and acknowledged, defenders must act quickly to ensure their deployments are updated to mitigate any potential risks associated with this issue.
Vulnerability Details
The vulnerability allows for a potential segfault/denial of service. The CVSS 3.1 vector string indicates a local attack vector with low complexity, requiring low privileges and no user interaction. The affected product is TensorFlow, a widely used machine learning library developed by Google.
Technical Analysis
The root cause of this vulnerability stems from TensorFlow's handling of quantized types in its API, specifically in the context of `tf.compat.v1.*` operations. When these operations are invoked without the necessary support for the quantized types, a nullptr is dereferenced, which leads to a segmentation fault.
The attack vector for this vulnerability is local, meaning that an attacker needs to have local access to exploit it. The complexity of executing this attack is low, as it does not require any sophisticated techniques. The attacker must have low privileges, and there is no requirement for user interaction.
The impacts include a high availability risk, as the application can crash due to this flaw. However, there are no impacts on confidentiality or integrity.
Risk & Impact Analysis
Risk to organizations includes potential service interruption leading to denial of service. The availability impact is marked as high, indicating that this vulnerability could lead to severe operational disruptions if not addressed. Given the widespread use of TensorFlow in machine learning applications, the blast radius could extend across multiple systems relying on this library.
Organizations should address this vulnerability in their priority patch cycle to mitigate risks of exploitation and ensure operational continuity.
Exploitation Status
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
The affected versions include TensorFlow versions prior to 2.9.0, specifically: 2.8.1, 2.7.2, and 2.6.4. If version information is missing, it is stated that all versions prior to vendor patch are vulnerable.
Mitigation & Remediation
Organizations should ensure they update to TensorFlow versions 2.9.0 or later to mitigate this vulnerability. If immediate patching isn't feasible, consider implementing configuration hardening to limit exposure. Additional network controls may also help, including monitoring for any unusual activity that may indicate exploitation attempts.
Detection Guidance
Monitoring logs for segfault errors can help detect attempts to exploit this vulnerability. Behavioral anomalies in TensorFlow operations should also be noted, as they may indicate an ongoing attack.
AppSecure Threat Intelligence Insight
The long-term significance of this vulnerability highlights the importance of maintaining updated libraries, especially for widely used frameworks like TensorFlow. Security teams should remain vigilant in monitoring for vulnerabilities that could lead to denial of service. Effective vulnerability management programs are crucial for identifying and mitigating risks promptly.
For further reading on vulnerability management, organizations can explore the following resources: vulnerability management program and the best practices in penetration testing methodology.
Additionally, organizations can improve their security posture by focusing on security testing best practices to identify potential weaknesses proactively.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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