EUVD-2024-38935

Comprehensive Technical Analysis of EUVD-2024-38935

1. Vulnerability Assessment and Severity Evaluation

Vulnerability Description: The vulnerability in streamlit-geospatial, a Streamlit multipage app for geospatial applications, involves the vis_params variable on line 1254 in pages/1_📷_Timelapse.py. This variable takes user input, which is later used in the eval() function on line 1345, leading to remote code execution (RCE).

Severity Evaluation:

• Base Score: 9.8
• Base Score Version: CVSS:3.1
• Base Score Vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

The CVSS score of 9.8 indicates a critical vulnerability. The attack vector (AV:N) is network-based, requiring low complexity (AC:L) and no privileges (PR:N) or user interaction (UI:N). The impact on confidentiality, integrity, and availability is high (C:H/I:H/A:H).

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 evaluated by the eval() function. This allows the attacker to execute arbitrary code on the server.

Exploitation Methods:

• Code Injection: By crafting a specially designed input, an attacker can inject Python code that will be executed by the eval() function. This can lead to data exfiltration, system compromise, or further exploitation of the server.

3. Affected Systems and Software Versions

Affected Software:

• streamlit-geospatial versions prior to commit c4f81d9616d40c60584e36abb15300853a66e489.

Affected Systems:

• Any system running the vulnerable version of streamlit-geospatial, particularly those with network exposure.

4. Recommended Mitigation Strategies

Immediate Mitigation:

• Update Software: Upgrade to the version of streamlit-geospatial that includes commit c4f81d9616d40c60584e36abb15300853a66e489 or later.
• Input Validation: Implement strict input validation and sanitization for user inputs to prevent code injection.
• Avoid eval(): Refactor the code to avoid using the eval() function, which is inherently risky.

Long-Term Mitigation:

• Security Audits: Conduct regular security audits and code reviews to identify and mitigate similar vulnerabilities.
• Patch Management: Implement a robust patch management process to ensure timely updates and patches.

5. Impact on European Cybersecurity Landscape

Impact Analysis:

• Critical Infrastructure: Geospatial applications are often used in critical infrastructure, such as urban planning, environmental monitoring, and disaster management. A vulnerability in such applications can have severe consequences, including data breaches and operational disruptions.
• Data Integrity: Compromised geospatial data can lead to incorrect decision-making, affecting public safety and economic activities.
• Regulatory Compliance: Organizations must ensure compliance with European regulations such as GDPR, which mandates the protection of personal data.

6. Technical Details for Security Professionals

Vulnerable Code Snippet:

# pages/1_📷_Timelapse.py
vis_params = user_input  # Line 1254
eval(vis_params)  # Line 1345

Fixed Code Snippet:

# pages/1_📷_Timelapse.py
vis_params = sanitize_input(user_input)  # Line 1254
safe_eval(vis_params)  # Line 1345

References:

• GitHub Security Advisory
• Commit Fix
• Vulnerable Code Line 1254
• Vulnerable Code Line 1345

Conclusion: The vulnerability in streamlit-geospatial is critical and requires immediate attention. Organizations should prioritize updating their software and implementing robust security measures to mitigate the risk of remote code execution. Regular security audits and adherence to best practices will help in maintaining a secure cyber environment.