EUVD-2024-38938

Comprehensive Technical Analysis of EUVD-2024-38938

1. Vulnerability Assessment and Severity Evaluation

Vulnerability Overview: The vulnerability in question affects the streamlit-geospatial application, specifically in the 8_🏜️_Raster_Data_Visualization.py file. Prior to commit c4f81d9616d40c60584e36abb15300853a66e489, the vis_params variable on line 80 takes user input, which is later used in the eval() function on line 86. This leads to a remote code execution (RCE) vulnerability.

Severity Evaluation: The vulnerability has a CVSS Base Score of 9.8, which is considered critical. The CVSS vector CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H indicates the following:

• Attack Vector (AV): Network (N)
• Attack Complexity (AC): Low (L)
• Privileges Required (PR): None (N)
• User Interaction (UI): None (N)
• Scope (S): Unchanged (U)
• Confidentiality (C): High (H)
• Integrity (I): High (H)
• Availability (A): High (H)

This high severity score underscores the critical nature of the vulnerability, which can be exploited remotely with low complexity and without requiring any privileges or user interaction.

2. Potential Attack Vectors and Exploitation Methods

Attack Vectors:

• Remote Code Execution (RCE): An attacker can inject malicious code through the vis_params variable, which is then executed by the eval() function. This can lead to arbitrary code execution on the server.
• Data Manipulation: Attackers can manipulate the input to execute commands that alter data, exfiltrate sensitive information, or disrupt services.

Exploitation Methods:

• Crafted Input: An attacker can craft specific input values for vis_params that, when evaluated, execute malicious code.
• Automated Scripts: Attackers can use automated scripts to repeatedly send malicious input to the application, increasing the likelihood of successful exploitation.

3. Affected Systems and Software Versions

Affected Systems:

• Any system running the streamlit-geospatial application with versions prior to commit c4f81d9616d40c60584e36abb15300853a66e489.

Software Versions:

• streamlit-geospatial versions < c4f81d9616d40c60584e36abb15300853a66e489.

4. Recommended Mitigation Strategies

Immediate Mitigation:

• Update Software: Immediately update to the version that includes commit c4f81d9616d40c60584e36abb15300853a66e489 or later.
• Input Validation: Implement strict input validation and sanitization for the vis_params variable to prevent malicious input.
• Disable eval(): Avoid using the eval() function for processing user input. Use safer alternatives like JSON parsing or predefined functions.

Long-Term Mitigation:

• Code Review: Conduct thorough code reviews to identify and mitigate similar vulnerabilities.
• Security Training: Provide security training for developers to avoid using unsafe functions like eval().
• Regular Updates: Ensure regular updates and patches are applied to all software components.

5. Impact on European Cybersecurity Landscape

Potential Impact:

• Data Breaches: Exploitation of this vulnerability can lead to data breaches, exposing sensitive information.
• Service Disruption: Attackers can disrupt services, leading to operational downtime and financial losses.
• Reputation Damage: Organizations using the affected software may suffer reputational damage due to security incidents.

Regulatory Compliance:

• GDPR: Organizations must ensure compliance with GDPR by protecting personal data from unauthorized access and breaches.
• NIS Directive: Critical infrastructure providers must adhere to the NIS Directive, ensuring robust cybersecurity measures are in place.

6. Technical Details for Security Professionals

Vulnerability Details:

• File: 8_🏜️_Raster_Data_Visualization.py
• Lines Affected: 80 (input handling), 86 (eval() function)
• Commit Fix: c4f81d9616d40c60584e36abb15300853a66e489

References:

• Advisory: GitHub Security Lab Advisory
• Commit Fix: GitHub Commit
• Source Code: Affected Lines

Additional Information:

• CVE ID: CVE-2024-41119
• EPSS Score: 2 (indicating a low likelihood of exploitation in the wild, but still a significant risk due to the severity)

Conclusion: The vulnerability in streamlit-geospatial is critical and requires immediate attention. Organizations should prioritize updating to the patched version and implementing robust input validation to mitigate the risk of remote code execution. Regular security audits and adherence to best practices will help maintain a strong cybersecurity posture.