threat-modeling.md

Threat Model Analysis for openinterpreter/open-interpreter

Threat: Unintended File System Access

Description: An attacker, through prompt injection or by exploiting model behavior, could cause the language model to generate code that reads, writes, or deletes files on the server's file system outside of the application's intended scope. This could be achieved by instructing the model to use file system commands within the generated code (e.g., Python's os module, shell commands).

Impact: Confidentiality breach (reading sensitive files), data integrity compromise (modifying or deleting important files), system instability or denial of service (deleting system files).

Affected Component: open-interpreter's code execution module, specifically when interacting with the operating system's file system.

Risk Severity: High

Mitigation Strategies:

• Sandboxing: Run open-interpreter in a sandboxed environment with restricted file system access.
• Input Validation: Carefully validate and sanitize user inputs to prevent prompt injection attacks that could lead to malicious file system operations.
• Principle of Least Privilege: Ensure the application and open-interpreter process run with minimal file system permissions.
• Output Monitoring: Monitor the generated code and executed commands for suspicious file system operations.
• File System Whitelisting: If possible, restrict open-interpreter's file system access to a specific whitelist of directories.

Threat: Arbitrary Code Execution via Prompt Injection

Description: An attacker crafts a malicious prompt that, when processed by the language model, results in the generation and execution of arbitrary code on the server. This could involve injecting code directly into the prompt or manipulating the model's context to influence code generation.

Impact: Full system compromise, data breach, denial of service, malware installation, and complete control over the application and server.

Affected Component: open-interpreter's core language model interaction and code execution engine.

Risk Severity: Critical

Mitigation Strategies:

• Robust Input Sanitization and Validation: Implement strict input validation and sanitization for all user inputs and external data used in prompts.
• Prompt Engineering: Design prompts carefully to minimize the possibility of unintended code generation and limit the model's scope.
• Output Validation and Filtering: Inspect and validate the generated code before execution. Implement filters to block potentially harmful code patterns.
• Sandboxing and Isolation: Run open-interpreter in a secure sandbox or isolated environment to limit the impact of successful code execution attacks.
• Principle of Least Privilege: Run the application and open-interpreter with the least necessary privileges.

Threat: Network Exfiltration through Generated Code

Description: An attacker could manipulate the language model to generate code that establishes network connections and exfiltrates sensitive data from the server to an external attacker-controlled server. This could be achieved by instructing the model to use network libraries (e.g., requests, socket) in the generated code.

Impact: Confidentiality breach, data leakage of sensitive application data, user data, or internal system information.

Affected Component: open-interpreter's code execution module, specifically when interacting with network functionalities.

Risk Severity: High

Mitigation Strategies:

• Network Segmentation: Isolate the server running open-interpreter in a network segment with restricted outbound network access.
• Firewall Rules: Implement strict firewall rules to control outbound network connections from the server running open-interpreter.
• Output Monitoring: Monitor network activity originating from the open-interpreter process for suspicious outbound connections.
• Network Access Control: Limit the network capabilities available to the open-interpreter process.
• Content Security Policy (CSP): If applicable, use CSP to restrict network requests initiated by the application.

Threat: Exposure of API Keys and Secrets in Logs/Code

Description: If the application provides API keys, database credentials, or other secrets to the language model's context (e.g., in prompts or environment variables accessible to the model), the generated code might inadvertently log these secrets, expose them in error messages, or use them insecurely, making them accessible to attackers.

Impact: Unauthorized access to external services, data breaches, financial loss, and compromise of linked accounts.

Affected Component: open-interpreter's context handling and code generation, application's secret management practices.

Risk Severity: High

Mitigation Strategies:

• Secret Management: Avoid passing secrets directly to the language model's context. Use secure secret management practices (e.g., environment variables, dedicated secret stores) and access secrets programmatically within the application code, not directly in prompts.
• Input Sanitization: Sanitize prompts to remove any potentially exposed secrets before sending them to the language model.
• Output Filtering: Filter generated code and outputs to redact or remove any accidentally exposed secrets.
• Logging Security: Securely configure logging to prevent logging of sensitive data, including API keys and secrets.
• Principle of Least Privilege: Grant the open-interpreter process only the necessary permissions to access secrets.