CVE-2022-29216 is a high-severity vulnerability affecting Google TensorFlow, specifically prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4. The vulnerability arises from a code injection flaw in TensorFlow's `saved_model_cli` tool, which can potentially be exploited to open a reverse shell. Although this tool is typically executed manually, the risk is significant enough to warrant immediate attention. TensorFlow maintainers have addressed the issue by removing the `safe=False` argument in parsing, ensuring all input is handled securely.
The CVSS score for this vulnerability is 7.8, indicating high severity. The scoring reflects a local attack vector with low complexity and minimal privileges required, making it more accessible for exploitation. Given the nature of machine learning applications and their integration into various environments, organizations utilizing TensorFlow need to prioritize remediation efforts.
Risk to organizations includes unauthorized access and potential control over systems running impacted TensorFlow versions. As such, organizations should prioritize patching immediately. The patched versions are 2.9.0, 2.8.1, 2.7.2, and 2.6.4, which contain mitigations for this vulnerability.
At present, there are no known public exploits or proof-of-concept (PoC) code available, which suggests that while the vulnerability is critical, it has not yet been widely exploited in the wild. Nevertheless, organizations should maintain vigilance and apply security practices to mitigate the risks associated with code injection vulnerabilities.
In summary, TensorFlow's `saved_model_cli` vulnerability (CVE-2022-29216) poses a high risk, and organizations leveraging TensorFlow must act swiftly to apply the updates to safeguard their environments.
Vulnerability Details
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, TensorFlow's `saved_model_cli` tool is vulnerable to a code injection. This can be used to open a reverse shell. This code path was maintained for compatibility reasons as the maintainers had several test cases where numpy expressions were used as arguments. However, given that the tool is always run manually, the impact of this is still not severe. The maintainers have now removed the `safe=False` argument, so all parsing is done without calling `eval`. The patch is available in versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4.
Technical Analysis
The root cause of this vulnerability is related to improper handling of user input in the `saved_model_cli` tool, which can lead to code injection if malicious input is provided. The attack vector is local, requiring the attacker to have access to the system where TensorFlow is installed. The attack complexity is rated as low, as it does not require extensive technical skills to exploit. Privileges required are also low, meaning an attacker with limited access could leverage this vulnerability for further exploitation.
User interaction is not required to exploit this vulnerability, which increases the risk of exploitation. Once exploited, confidentiality, integrity, and availability impacts are all rated as high, highlighting the significant potential damage an attacker could cause. This risk profile makes it essential for organizations to address this vulnerability swiftly.
Risk & Impact Analysis
The deployment of TensorFlow in various sensitive environments increases the risk associated with this vulnerability. Organizations using TensorFlow for machine learning applications must recognize the potential for unauthorized access and remote code execution. The blast radius could be significant, especially in systems that do not have robust network segmentation or access controls. Given the high CVSS score, organizations should address this vulnerability in their priority patch cycle.
The urgency for addressing CVE-2022-29216 is underscored by the potential impact on machine learning workflows and data integrity. As organizations continue to integrate machine learning into their operations, ensuring the security of the underlying frameworks becomes critical. The presence of this vulnerability highlights the need for continuous monitoring and proactive security measures.
Signal | Status |
|---|---|
Known Exploit | No |
Public PoC | No |
Actively Exploited | No |
Ransomware Use | No |
Affected Versions
The vulnerable versions of TensorFlow include all versions prior to 2.9.0, as well as 2.8.1, 2.7.2, and 2.6.4. Organizations must ensure they upgrade to patched versions 2.9.0, 2.8.1, 2.7.2, or 2.6.4 to mitigate the risk associated with this vulnerability.
Mitigation & Remediation
Organizations should prioritize upgrading TensorFlow to versions 2.9.0, 2.8.1, 2.7.2, or 2.6.4 to ensure that the vulnerability is patched. If immediate upgrading is not feasible, consider implementing workarounds such as restricting access to the `saved_model_cli` tool to trusted users only. Additionally, monitoring and logging access to TensorFlow installations can help identify potential abuse of the tool.
For more information on securing TensorFlow installations, organizations can refer to our application security assessment services.
Detection Guidance
Security teams should monitor logs for any unusual access patterns involving the `saved_model_cli` tool. Look for unexpected command executions, especially those involving potentially unsafe inputs. Additionally, implement network monitoring to detect any unauthorized attempts to access TensorFlow installations.
AppSecure Threat Intelligence Insight
This vulnerability underscores the critical nature of input validation in software development, particularly in tools that handle user-generated data. Security teams should adopt a penetration testing methodology to evaluate the security posture of their applications, ensuring that input validation and parsing logic are robust against injection attacks.
As machine learning continues to evolve, organizations should remain vigilant about the security of their frameworks. The TensorFlow vulnerability serves as a reminder to implement best practices in software development, including regular security assessments and code reviews to identify and remediate vulnerabilities proactively.
For further insights on securing machine learning frameworks, organizations can explore our API penetration testing guide and other resources.
Disclaimer: This content was generated using AI. While we strive for accuracy, please verify critical information with official sources.
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