threat-modeling.md

Threat Model Analysis for github/scientist

Threat: Candidate Code Injection

Description: An attacker injects malicious code that is executed as the "candidate" code path within a Scientist experiment. This exploits vulnerabilities in how the candidate code is defined, loaded, or deployed, allowing the attacker to run arbitrary code on the server. Scientist's core purpose is to run this candidate code, making it a direct target.

• Impact: Complete system compromise, data breaches, unauthorized actions, denial of service.
• Affected Component: Experiment definition and execution (science block, use, try methods). The mechanism for loading and executing the candidate code is the primary target. Scientist's core functionality is directly involved.
• Risk Severity: Critical
• Mitigation Strategies:
  • Strict Code Review: All candidate code must undergo rigorous code review and security testing.
  • Secure Deployment: Implement secure deployment pipelines with strong authentication, authorization, and integrity checks (e.g., code signing).
  • Input Validation: If candidate code is somehow influenced by user input (strongly discouraged), apply strict input validation.
  • Least Privilege: Run the application with the least necessary privileges.
  • Avoid Dynamic Code Loading: If possible, avoid dynamic loading of candidate code. If unavoidable, use secure code loading mechanisms with strong verification.

Threat: Experiment Context Manipulation

Description: An attacker manipulates the input or environment (context) provided to the Scientist experiment. They craft inputs that cause the control and candidate paths to behave differently, specifically to mask malicious behavior in the candidate or to trigger false discrepancies. This directly targets Scientist's context handling.

• Impact: Masking of malicious code, false positives/negatives in experiment results, leading to incorrect conclusions.
• Affected Component: Scientist::Experiment#context and how it's used within the science block. Code that sets or modifies the experiment context is vulnerable. This is a direct attack on Scientist's context mechanism.
• Risk Severity: High
• Mitigation Strategies:
  • Context Isolation: Ensure the context passed to control and candidate paths is identical and immutable. Deep-copy if necessary.
  • Input Validation: Validate all inputs that contribute to the experiment context.
  • Context Logging: Log the complete experiment context for auditing.
  • Deterministic Context: Design the experiment to be as deterministic as possible.

Threat: Experiment Result Tampering

Description: An attacker gains access to the storage or communication channel used by Scientist to publish experiment results. They modify the results to hide discrepancies or create false ones, directly influencing decisions based on the experiment. This targets Scientist's result reporting mechanism.

• Impact: Incorrect deployment decisions, masking of bugs or vulnerabilities, potential data breaches if results contain sensitive information.
• Affected Component: Scientist::Result and the configured publish method (and the underlying publisher implementation). The storage mechanism for results is also a target. This directly impacts Scientist's reporting.
• Risk Severity: High
• Mitigation Strategies:
  • Secure Publisher: Use a secure result publisher with authentication, authorization, and encryption (e.g., TLS).
  • Data Integrity: Implement integrity checks (e.g., checksums, digital signatures) on stored results.
  • Access Control: Restrict access to the result storage.
  • Auditing: Monitor access to and modifications of experiment results.

Threat: Experiment Configuration Tampering

Description: An attacker modifies the Scientist experiment configuration (enabling/disabling, sampling rate). They might disable an experiment to prevent detection of malicious candidate code or increase the sampling rate to amplify a DoS attack. This targets how Scientist experiments are controlled.

• Impact: Masking of malicious code, denial of service, incorrect experiment results.
• Affected Component: The mechanism for storing and loading experiment configurations. The enabled? check within the science block. This directly affects Scientist's control mechanisms.
• Risk Severity: High
• Mitigation Strategies:
  • Secure Configuration Storage: Protect the configuration with strong access controls and auditing.
  • Configuration Validation: Validate configuration values to prevent invalid settings.
  • Rate Limiting: Implement rate limiting on configuration changes.
  • Alerting: Monitor for unexpected configuration changes and trigger alerts.

Threat: Sensitive Data Leakage in Results

Description: The control or candidate code paths handle sensitive data, and this data is inadvertently included in the experiment results published by Scientist. This is a direct consequence of how Scientist captures and reports results.

• Impact: Data breach, privacy violations, compliance issues.
• Affected Component: Scientist::Result and the data captured within the science block (return values, logged data). The publish method and publisher. This is inherent to Scientist's result reporting.
• Risk Severity: High
• Mitigation Strategies:
  • Data Minimization: Avoid logging sensitive data directly in results.
  • Data Sanitization: Implement data sanitization/masking before logging.
  • Secure Publisher: Use a secure result publisher with encryption and access controls.
  • Data Retention Policies: Implement data retention policies for experiment results.

Threat: Candidate Code Denial of Service

Description: The candidate code path (executed by Scientist's try block) contains bugs or inefficiencies that cause performance degradation, resource exhaustion, or crashes. Because Scientist runs both paths, this impacts overall application availability. This is a direct consequence of Scientist running the candidate code.

• Impact: Reduced application performance, denial of service, potential outage.
• Affected Component: The try block and the candidate code itself. The overall experiment execution within the science block. This is directly related to Scientist's execution of the candidate.
• Risk Severity: High
• Mitigation Strategies:
  • Thorough Testing: Extensively test candidate code for performance and stability before production.
  • Low Sampling Rate: Start with a very low sampling rate (e.g., 1%).
  • Resource Monitoring: Monitor resource usage and set alerts.
  • Circuit Breakers: Implement circuit breakers to isolate the candidate code.
  • Timeouts: Use timeouts when executing the candidate code. This is critical.

Threat: Candidate Code Authorization Bypass

Description: The candidate code path (executed by Scientist) has different/weaker authorization logic than the control path. It allows unauthorized actions, even if the results are discarded. The side effects of the candidate code are the problem, and Scientist is the enabler of these side effects.

• Impact: Unauthorized access to data/functionality, data modification, privilege escalation.
• Affected Component: The try block and the candidate code's authorization logic. The ignore block (or lack thereof) is crucial. Scientist's execution of the candidate is the direct enabler.
• Risk Severity: Critical
• Mitigation Strategies:
  • Identical Authorization: Ensure the candidate code enforces the exact same authorization as the control path.
  • Centralized Authorization: Use a centralized authorization framework.
  • Sandboxing (Ideal but Difficult): Ideally, run candidate code in a sandboxed environment.
  • Careful Exception Handling: Ensure exception handling around the candidate code (e.g., rescue blocks) does not bypass authorization or allow unauthorized actions. Scientist's default exception handling needs very careful consideration. Explicitly re-raise authorization exceptions.