threat-modeling.md

Threat Model Analysis for streamlit/streamlit

Threat: Arbitrary Python Code Injection via User Input

Description: An attacker crafts malicious input through Streamlit widgets (like st.text_input, st.file_uploader, or st.selectbox) that, when processed by the application's Python backend, executes arbitrary code on the server. This is possible if user input is directly used in functions like exec(), eval(), or passed to shell commands without proper sanitization within the Streamlit application code.

Impact: Full server compromise, unauthorized data access, data breaches, denial of service, and potential lateral movement within the network.

Streamlit Component Affected: Input widgets (st.text_input, st.number_input, st.file_uploader, st.selectbox, etc.), URL parameters accessed via st.experimental_get_query_params.

Risk Severity: Critical

Mitigation Strategies:

• Strict Input Validation: Implement rigorous input validation and sanitization for all user-provided data obtained through Streamlit widgets before using it in any Python code execution or system calls.
• Avoid Dynamic Code Execution: Minimize or completely avoid using dynamic code execution functions like exec() or eval() with user-provided input within the Streamlit application.
• Parameterized Queries (for Databases): When interacting with databases, always use parameterized queries or ORMs to prevent SQL injection if user input influences database queries.
• Code Review for Injection Points: Conduct thorough code reviews specifically looking for areas where user input from Streamlit widgets is directly used in potentially unsafe operations.
• Principle of Least Privilege: Run the Streamlit application process with the minimum necessary privileges to limit the impact of successful code injection.
• Sandboxing/Containerization: Deploy the Streamlit application within a sandboxed environment or container to further isolate it and limit the damage from code execution vulnerabilities.

Threat: Deserialization Vulnerabilities in Cached Data

Description: An attacker crafts malicious serialized data and injects it into Streamlit's caching mechanism (@st.cache_data, @st.cache_resource). When Streamlit attempts to retrieve and deserialize this cached data, it executes the malicious code embedded within it. This could be achieved by exploiting weaknesses in how Streamlit manages cache keys or the underlying serialization library.

Impact: Remote code execution, data corruption, denial of service, potentially allowing the attacker to gain control of the Streamlit application server.

Streamlit Component Affected: Caching decorators (@st.cache_data, @st.cache_resource).

Risk Severity: High

Mitigation Strategies:

• Secure Serialization Practices: Avoid caching untrusted data or data derived from untrusted sources using Streamlit's caching mechanisms. If caching is necessary, use secure serialization formats like JSON and avoid Python's pickle for untrusted data.
• Cache Integrity Checks: Implement integrity checks (e.g., cryptographic signatures or checksums) for cached data to detect if it has been tampered with.
• Limit Cache Scope: Carefully define the scope and lifetime of cached data to minimize the window of opportunity for attackers to inject malicious data.
• Regular Streamlit Updates: Keep Streamlit and its dependencies updated to the latest versions to patch any known deserialization vulnerabilities within the framework itself.

Threat: Data Exposure through Streamlit UI Elements

Description: Developers, due to the rapid development nature of Streamlit, might inadvertently display sensitive information (API keys, database credentials, Personally Identifiable Information - PII, internal system details) directly in the Streamlit UI using elements like st.write, st.dataframe, or st.code. Unauthorized users accessing the application through the Streamlit interface can then view this sensitive data.

Impact: Confidentiality breach, exposure of sensitive credentials leading to further system compromise, regulatory compliance violations (e.g., GDPR, HIPAA), reputational damage.

Streamlit Component Affected: All UI output elements (st.write, st.dataframe, st.table, st.json, st.code, st.secrets, etc. if misused).

Risk Severity: High

Mitigation Strategies:

• Implement Access Control: Enforce authentication and authorization within the Streamlit application to restrict access to sensitive features and data based on user roles.
• Avoid Displaying Sensitive Data in UI: Refrain from directly displaying sensitive information in the Streamlit UI. Use secure logging mechanisms for debugging and monitoring instead.
• Data Redaction and Masking: If sensitive data must be displayed, redact or mask it appropriately in the UI to protect confidentiality.
• Secure Secrets Management: Utilize Streamlit's st.secrets for managing sensitive credentials and configuration, ensuring they are not hardcoded in the application code and are accessed securely.
• Code Review for Data Leaks: Conduct thorough code reviews to identify and eliminate any instances where sensitive data is unintentionally exposed through Streamlit UI elements.

Threat: Malicious Custom Components

Description: Developers integrate custom Streamlit components, potentially from untrusted or unverified sources (including those loaded via st.components.v1.iframe, st.components.v1.html, or external component libraries). These components could contain malicious JavaScript or Python code designed to steal data displayed in the Streamlit application, compromise the user's browser, or even attempt to interact with the Streamlit server in unintended ways.

Impact: Data theft from the Streamlit application UI, cross-site scripting (XSS) vulnerabilities affecting users, potential for remote code execution if the component interacts with the server-side Python code maliciously, backdoors introduced into the application.

Streamlit Component Affected: Custom components (st.components.v1.iframe, st.components.v1.html, externally developed components).

Risk Severity: High

Mitigation Strategies:

• Trusted Component Sources: Only use custom Streamlit components from highly trusted and reputable sources with a proven security track record.
• Component Code Review: Thoroughly review the source code of any custom component before integrating it into the Streamlit application, especially components from unknown or less reputable sources. Pay close attention to JavaScript and Python code within the component.
• Component Vetting Process: Establish a formal vetting process for evaluating and approving custom components before they are allowed to be used in production Streamlit applications.
• Content Security Policy (CSP): Implement a Content Security Policy (CSP) to restrict the capabilities of custom components, limiting their access to resources and mitigating potential XSS risks.
• Isolate Components (if possible): Explore methods to isolate custom components within sandboxed iframes or similar mechanisms to limit their potential impact on the main Streamlit application and server.

Threat: Vulnerabilities in Streamlit Components and Dependencies

Description: Streamlit itself, or its underlying Python and JavaScript dependencies, may contain security vulnerabilities. Attackers can exploit these vulnerabilities to compromise the Streamlit application. This includes vulnerabilities in the core Streamlit library, official Streamlit components, and third-party libraries used by Streamlit or custom components.

Impact: Remote code execution, denial of service, data breaches, cross-site scripting (XSS), and other security issues depending on the nature of the vulnerability. Exploitation could lead to full control of the Streamlit server or compromise of user sessions.

Streamlit Component Affected: Streamlit core library, Streamlit components (both official and custom), underlying Python and JavaScript dependencies.

Risk Severity: High (potential for Critical depending on vulnerability)

Mitigation Strategies:

• Regular Updates - Streamlit and Dependencies: Maintain a rigorous patching schedule to regularly update Streamlit and all its dependencies (Python libraries, JavaScript libraries, etc.) to the latest versions. This is crucial for addressing known security vulnerabilities.
• Dependency Scanning and Management: Implement automated dependency scanning tools to continuously monitor for known vulnerabilities in Streamlit's dependencies. Use dependency management tools to track and manage versions effectively.
• Pin Dependency Versions: Pin dependency versions in your project's requirements files to ensure consistent and predictable application behavior and to control when updates are applied, allowing for testing before deployment.
• Security Monitoring and Alerts: Set up security monitoring and alerts to be notified of newly discovered vulnerabilities affecting Streamlit or its dependencies.
• Stay Informed about Streamlit Security Advisories: Regularly monitor Streamlit's official channels and security advisories for announcements regarding security vulnerabilities and recommended mitigation steps.