threat-modeling.md

Threat Model Analysis for github/scientist

Threat: Malicious Experiment Definition Injection

• Threat: Malicious Experiment Definition Injection
• Description: If experiment definitions are dynamically generated based on external input (highly discouraged), an attacker could inject malicious code or configurations into the experiment definition. This could involve manipulating input data that is used to construct the experiment block, potentially leading to the execution of arbitrary code within the use or try blocks of the experiment.
• Impact: Code injection, arbitrary code execution, data manipulation, denial of service, complete compromise of the application, potential lateral movement within the infrastructure.
• Affected Scientist Component: Experiment definition (Scientist.run, dynamic construction of experiment block, use, try methods).
• Risk Severity: High
• Mitigation Strategies:
  • Avoid dynamic experiment definition: Strongly discourage dynamically constructing experiment definitions based on external or untrusted input.
  • Input validation and sanitization: If dynamic definition is unavoidable, rigorously validate and sanitize all external inputs used in experiment construction.
  • Code review and security testing: Thoroughly review and security test any code that dynamically generates experiment definitions.
  • Principle of least privilege: Run the application with the minimum necessary privileges to limit the impact of potential code execution vulnerabilities.
  • Web Application Firewall (WAF): If input is coming from web requests, consider using a WAF to detect and block malicious injection attempts.

Threat: Vulnerabilities Introduced in Candidate Path Code

• Threat: Vulnerabilities Introduced in Candidate Path Code
• Description: Candidate paths, being new or modified code, are more likely to contain security vulnerabilities (injection flaws, logic errors, resource leaks, etc.) compared to the established control path. Running these potentially vulnerable candidate paths in parallel, even in a non-production context, can expose the application to these vulnerabilities. An attacker exploiting a vulnerability in the candidate path could potentially gain unauthorized access, manipulate data, or cause denial of service.
• Impact: Introduction of new vulnerabilities into the application, data corruption if candidate path interacts with data stores, denial of service if candidate path is resource intensive or crashes the application, potential escalation of privileges.
• Affected Scientist Component: Candidate path code (try method, code executed within the experiment).
• Risk Severity: High
• Mitigation Strategies:
  • Secure coding practices: Adhere to secure coding practices when developing candidate path code, including input validation, output encoding, and avoiding common vulnerabilities (e.g., SQL injection, cross-site scripting).
  • Static and dynamic code analysis: Utilize static and dynamic code analysis tools to identify potential vulnerabilities in candidate path code before deployment.
  • Thorough testing: Conduct comprehensive security testing of candidate paths, including unit tests, integration tests, and penetration testing.
  • Code review: Perform thorough code reviews of candidate path code by security-conscious developers.
  • Isolate candidate path execution: If possible, execute candidate paths in isolated environments or sandboxes to limit the impact of potential vulnerabilities.