This vulnerability allows TensorFlow to allocate a large vector during shape inference based on a user-controlled tensor value. The CVSS score for this vulnerability is 6.5, indicating a medium severity level. The impact on availability is high, which poses a risk to organizations that utilize this open-source machine learning framework.
Organizations should prioritize patching immediately, as this vulnerability can lead to crashes and service disruptions. The fix is included in TensorFlow version 2.8.0, with backports available for earlier versions still in support.
The vulnerability has been classified as medium severity due to its potential for significant availability impact. Though not classified as high-profile, it represents a real threat that can be exploited if not promptly addressed.
Currently, there are no known public exploits for this vulnerability, and it is not listed in the Known Exploited Vulnerabilities (KEV) catalog. Nevertheless, proactive measures are essential to safeguard systems.
Vulnerability Details
The official description states that during shape inference, TensorFlow can allocate a large vector based on a value from a tensor controlled by the user. This flaw could potentially lead to application crashes.
The vulnerability falls under the categories of CWE-400 (Unbounded Resource Consumption) and CWE-1284 (Allocation of Resources Without Limits).
The CVSS 3.1 score of 6.5 reflects a medium severity level, with a low attack complexity and low privileges required for exploitation.
Technical Analysis
The root cause of this vulnerability lies in the way TensorFlow handles shape inference, allowing for potentially excessive resource allocation based on user-controlled inputs.
The attack vector is network-based, with low attack complexity. The requirement for low privileges means that an attacker can exploit this vulnerability without needing extensive access.
No user interaction is required, which increases the risk of exploitation. The impact on availability is rated as high, as the application may crash or become unresponsive.
Risk & Impact Analysis
Risk to organizations includes potential service disruption and loss of availability. If exploited, this vulnerability could cause significant downtime.
Organizations should assess their exposure to this vulnerability, particularly if they rely on TensorFlow for critical applications. The urgency for remediation is medium, given the potential for high availability impact.
Given the CVSS score of 6.5 and the absence of known exploits, organizations should schedule remediation during their priority patch cycle to mitigate risks.
Exploitation Status
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
Affected versions include TensorFlow 2.5.2 and earlier, as well as versions 2.6.0 to 2.6.2, and TensorFlow 2.7.0. All versions prior to vendor patch are affected.
Mitigation & Remediation
Organizations should upgrade to TensorFlow version 2.8.0 or later to mitigate this vulnerability. For those using earlier versions, TensorFlow 2.7.1, 2.6.3, and 2.5.3 will receive backported fixes.
In cases where immediate patching is not feasible, organizations should implement strict access controls and monitor for unusual behavior in TensorFlow applications.
For further guidance, organizations may consider our continuous penetration testing services to help identify and remediate vulnerabilities.
Detection Guidance
Monitoring logs for unexpected application behaviors and tracking resource allocation patterns can help detect potential exploitation of this vulnerability.
Identifying unusual spikes in resource usage may also indicate an attempt to exploit this vulnerability.
AppSecure Threat Intelligence Insight
This vulnerability highlights the importance of resource management in software development, particularly in machine learning frameworks like TensorFlow.
As machine learning applications continue to grow, organizations must maintain robust security postures that include regular updates and vulnerability assessments.
Security teams should integrate lessons learned from this vulnerability into broader security strategies to safeguard their applications against similar issues.
For more information on managing vulnerabilities effectively, organizations can refer to our vulnerability management program guide.
Additionally, our penetration testing methodology can provide insights into effective security practices.
Known Exploitation Timeline
This vulnerability is not currently listed in the KEV catalog, indicating no known exploitation at this time.
EPSS Risk Context
The EPSS score for this vulnerability is 0.003, placing it in the 53rd percentile. This suggests a relatively low probability of exploitation in the wild.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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