CVE-2022-23591 is a high-severity vulnerability found in Google TensorFlow, an open-source machine learning framework. This vulnerability allows self-recursive functions in the GraphDef format, which violates the framework's inherent assumptions. When a GraphDef containing self-referential structures is loaded, it can lead to a stack overflow during execution. The resolution for this issue is included in TensorFlow version 2.8.0, with backports provided for earlier versions, specifically TensorFlow 2.7.1, 2.6.3, and 2.5.3, which remain in a supported state. The urgency for defenders to address this vulnerability cannot be overstated.
The CVSS score for this vulnerability is 7.5, classifying it as high severity. The implications of this vulnerability are significant, particularly due to its potential to impact availability, as the stack overflow could cause runtime failures. Organizations relying on TensorFlow for critical systems should prioritize remediation to mitigate risks associated with this vulnerability.
As of now, there are no known exploits actively being utilized for this vulnerability in the wild. However, the nature of the issue highlights a real risk, which could be leveraged by attackers if left unaddressed. Organizations should take this vulnerability seriously and ensure they are running an updated and patched version of TensorFlow.
Given the potential impact of a stack overflow during execution, organizations are advised to act swiftly. Immediate patching is strongly encouraged, as failure to do so may result in operational disruptions and vulnerabilities to further exploits.
Vulnerability Details
The vulnerability is specifically related to the handling of self-recursive functions within the GraphDef format of TensorFlow. The runtime environment incorrectly assumes that self-referential functions are not present, leading to the potential for stack overflow errors when such functions are executed. The official description states, 'The fix will be included in TensorFlow 2.8.0.' This vulnerability has a CVSS score of 7.5, indicating a high level of risk due to its potential impact on system availability.
The affected product is TensorFlow, with specific version ranges outlined as vulnerable. These include versions up to 2.5.2, as well as versions 2.6.0 to 2.6.2, and 2.7.0. Organizations using these versions should take immediate action to upgrade or patch their installations.
Technical Analysis
The root cause of CVE-2022-23591 stems from the assumption within TensorFlow's GraphDef format that self-recursive functions are not allowed. Because the runtime does not validate this condition, the presence of such a function results in a stack overflow when trying to resolve its nodes. This vulnerability is categorized under CWE-400 (Uncontrolled Resource Consumption) and CWE-674 (Uncontrolled Recursion).
The attack vector is network-based, with low complexity and no privileges required for exploitation. Additionally, no user interaction is necessary, which significantly increases the threat level. While the confidentiality and integrity impacts are rated as none, the availability impact is rated as high due to the potential for causing application crashes.
Risk & Impact Analysis
Organizations deploying TensorFlow in production environments face a significant risk due to CVE-2022-23591. The potential for stack overflow can lead to application downtime, which can affect business continuity. The vulnerability's network attack vector means that it could be exploited remotely, increasing the chances of a successful attack.
The urgency for remediation is underscored by the CVSS score of 7.5, indicating a high severity. Organizations should prioritize patching this vulnerability as part of their immediate risk management strategy, especially as they assess the potential blast radius of exploiting this flaw.
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
The vulnerability affects TensorFlow versions 2.5.2 and earlier, as well as versions 2.6.0 through 2.6.2, and version 2.7.0. Organizations using these versions should update to TensorFlow 2.8.0 or later to mitigate this vulnerability.
Mitigation & Remediation
To remediate this issue, organizations should update to TensorFlow 2.8.0 or later. For those unable to upgrade immediately, consider implementing configuration hardening to limit the execution of potentially vulnerable GraphDef structures. Network controls should also be established to monitor and block any suspicious activities that may exploit this vulnerability. Organizations should validate remediation effectiveness through penetration testing to identify similar weaknesses.
Detection Guidance
Organizations should monitor their logs for indicators of unusual stack overflow errors during TensorFlow execution. Behavioral anomalies indicating unexpected failures in model execution should also be scrutinized. Additionally, network signatures may help detect attempts to exploit this vulnerability. System changes should be monitored closely to identify any unauthorized modifications.
AppSecure Threat Intelligence Insight
The long-term significance of CVE-2022-23591 lies in its representation of the challenges faced by complex frameworks like TensorFlow. This vulnerability highlights a pattern of oversight in validating function structures, which could lead to operational failures if not addressed. Security teams must learn from this incident and implement rigorous testing and validation practices to prevent similar vulnerabilities in the future.
To enhance security posture, organizations should consider adopting a comprehensive vulnerability management program that focuses on proactive identification and remediation of vulnerabilities. Additionally, organizations should stay informed about the latest security advisories to shield their systems against emerging threats.
Finally, it is crucial for organizations to regularly review their security practices and update their training programs, ensuring that all team members are aware of the latest security trends and vulnerabilities. Continuous education fosters a security-conscious culture that can significantly reduce the risk of exploitation.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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