CVE-2022-23587 is a high-severity vulnerability affecting Google TensorFlow, specifically its Grappler component. This vulnerability allows for an integer overflow during cost estimation for cropping and resizing operations. The implications of this flaw are significant, as the parameters for cropping are controlled by users, enabling attackers to potentially trigger undefined behavior.
The vulnerability has a CVSS score of 8.8, indicating a high level of risk. Attackers may leverage this weakness to affect confidentiality, integrity, and availability, making it critical for organizations utilizing TensorFlow to prioritize patching. The fix for this vulnerability will be addressed in TensorFlow version 2.8.0, with a patch also being applied to versions 2.7.1, 2.6.3, and 2.5.3.
Organizations should prioritize patching immediately to mitigate the associated risks. The vulnerability was published on February 4, 2022, and has been classified under CWE-190, which refers to integer overflow issues. Given that the vulnerability is still present in supported versions of TensorFlow, it is imperative to take action without delay.
As of now, there is no confirmed public exploit or proof of concept available, but the potential for exploitation exists due to the nature of the vulnerability. Organizations must remain vigilant and proactive in their remediation efforts.
Vulnerability Details
This vulnerability allows for an integer overflow in TensorFlow's Grappler component during cost estimation for crop and resize operations. The flaw exists in how user-controlled parameters are handled, leading to undefined behavior when manipulated maliciously.
The CVSS score of 9.8 (Critical) from NVD indicates that the vulnerability has a low attack complexity and requires low privileges, allowing attackers to exploit it with minimal effort. The attack vector is network-based, meaning that an attacker can exploit this vulnerability from a remote location.
TensorFlow versions 2.5.2 and earlier, along with 2.6.0 to 2.6.2, and 2.7.0 are confirmed to be vulnerable. The official patch will be included in TensorFlow version 2.8.0.
Technical Analysis
The root cause of this vulnerability stems from integer overflow during the cost estimation process for cropping and resizing operations. The attack vector is network-based, allowing exploitation without local access. The attack complexity is classified as low, as the necessary privileges required to exploit this vulnerability are also low.
User interaction is not required to exploit this vulnerability, making it easier for attackers to trigger the overflow. The impacts include high confidentiality, integrity, and availability risks, as a successful exploit can lead to undefined behavior in TensorFlow applications.
Risk & Impact Analysis
Risk to organizations includes potential data leaks, application crashes, or manipulation of TensorFlow operations, leading to unauthorized access or denial of service. The blast radius is extensive, as TensorFlow is widely utilized in machine learning applications across various sectors.
Urgency for remediation is high given the vulnerability's CVSS score of 9.8 and its presence in active environments. Organizations should assess their exposure to this vulnerability and prioritize the implementation of the patch as soon as it becomes available.
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
The following versions of TensorFlow are affected by this vulnerability: versions 2.5.2 and earlier, 2.6.0 through 2.6.2, and version 2.7.0. Organizations using these versions should update to the patched version 2.8.0 or later.
Mitigation & Remediation
To mitigate this vulnerability, organizations must apply the necessary patches as they are released. The fix for CVE-2022-23587 will be included in TensorFlow version 2.8.0. In the meantime, organizations may consider implementing workarounds such as restricting input parameters to the crop and resize operations.
For further assistance in identifying vulnerabilities and implementing effective security measures, organizations should seek professional penetration testing services.
Detection Guidance
Organizations should monitor logs for any anomalies related to TensorFlow operations, particularly those involving cropping and resizing functionalities. Behavioral indicators of potential exploitation may include unexpected crashes or performance degradation.
AppSecure Threat Intelligence Insight
CVE-2022-23587 highlights the ongoing risks associated with open-source software components, especially in widely used frameworks like TensorFlow. Organizations must remain vigilant regarding potential vulnerabilities and follow best practices for application security.
For organizations using TensorFlow, it is crucial to stay updated on security advisories and regularly assess their security posture through penetration testing methodology and vulnerability management programs to identify and remediate weaknesses in their environments.
This vulnerability serves as a reminder of the importance of maintaining secure coding practices and rigorous testing methodologies to prevent similar issues in the future. Continuous monitoring and proactive security measures are essential for safeguarding applications.
For further insights into security practices, organizations can explore our resources on API penetration testing and cloud penetration testing best practices.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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