Attack Surface: Experiment Configuration Injection
Description: Attackers inject malicious configurations into Scientist experiments, manipulating experiment behavior by controlling experiment names, enabled status, or even the definitions of control and candidate functions. This is possible when experiment configurations are loaded dynamically from untrusted sources and not properly validated before being passed to Scientist.
How Scientist Contributes: Scientist directly uses the provided configuration to define and execute experiments. If this configuration is compromised, Scientist becomes the execution engine for attacker-controlled logic. Scientist itself doesn't validate the configuration source.
Example: An application uses a database to store experiment configurations and passes these configurations to Scientist. An attacker gains write access to the database and modifies an experiment configuration to replace the candidate function with malicious code. When Scientist runs this experiment, it executes the attacker's code.
Impact:
- Denial of Service (DoS) by forcing experiments to always run or never run.
- Information Disclosure by manipulating experiment logging to expose sensitive data processed within experiments.
- Logic Manipulation leading to application compromise by altering experiment behavior to bypass security checks or execute malicious code within the experiment context managed by Scientist.
Risk Severity: Critical
Mitigation Strategies:
- Strict Input Validation for Scientist Configuration: Before passing any configuration to Scientist, rigorously validate and sanitize all input data. Ensure experiment names, function definitions, and other configuration parameters conform to expected formats and are safe.
- Secure Configuration Source for Scientist: Load experiment configurations from trusted and secure sources. Avoid directly using user input or publicly accessible databases as configuration sources for Scientist experiments. Prefer static configurations or secure configuration management systems.
- Principle of Least Privilege for Configuration Management: Implement strict access control to limit who can modify experiment configurations that are used by Scientist.
- Code Review of Scientist Configuration Loading: Thoroughly review the code responsible for loading and providing configurations to Scientist to identify and eliminate potential injection points.
Attack Surface: Logging and Reporting Vulnerabilities
Description: Vulnerabilities in the logging or reporting mechanisms used in conjunction with Scientist can be exploited. While Scientist itself doesn't dictate logging, it encourages and facilitates the logging of experiment results. If the application's logging implementation is flawed, Scientist's logging features can amplify the attack surface.
How Scientist Contributes: Scientist provides hooks and data points (control/candidate results, context) that are intended to be logged. This encourages logging, and if the application's logging is insecure, the data Scientist provides becomes part of the vulnerability. Scientist's design inherently leads to data being logged, increasing the potential impact of logging vulnerabilities.
Example: An application logs experiment context data provided to Scientist directly into a log file without sanitization. An attacker crafts a malicious payload within the experiment context. When Scientist executes the experiment and the application logs the context, the malicious payload is written to the logs, potentially leading to log injection vulnerabilities if these logs are processed insecurely later.
Impact:
- Log Injection vulnerabilities due to unsanitized data from Scientist being logged.
- Information Disclosure if experiment logs (containing data from Scientist experiments) are accessed by unauthorized parties.
- Data Integrity issues if experiment logs are tampered with, potentially affecting audit trails or analysis of Scientist experiments.
Risk Severity: High
Mitigation Strategies:
- Secure Logging Practices for Scientist Data: Implement secure logging practices specifically for data generated by Scientist experiments. Sanitize and encode all data obtained from Scientist before logging to prevent log injection.
- Secure Log Storage and Access Control: Securely store experiment logs generated from Scientist experiments with appropriate access controls. Encrypt logs if they contain sensitive information derived from or related to Scientist experiments.
- Regular Log Monitoring for Scientist Related Logs: Actively monitor logs related to Scientist experiments for suspicious patterns or anomalies that might indicate exploitation attempts.
- Secure Logging Infrastructure: Ensure the underlying logging infrastructure used for Scientist experiment logs is hardened and protected against common vulnerabilities.
Attack Surface: Data Leakage through Experiment Results
Description: Experiment results generated by Scientist, including control and candidate values and potentially experiment context, can inadvertently expose sensitive data if the application's reporting or handling of these results is insecure. This is especially critical if experiments process sensitive information.
How Scientist Contributes: Scientist's core function is to produce and compare experiment results. The very nature of Scientist is to generate data from experiments, and if these experiments involve sensitive data, Scientist's output becomes a potential source of leakage if not handled carefully by the application.
Example: An experiment within Scientist processes user Personally Identifiable Information (PII). The application, in its reporting of experiment results, inadvertently exposes raw PII data from the control or candidate branches in an API response or in an accessible dashboard displaying Scientist experiment outcomes.
Impact:
- Information Disclosure of sensitive data (PII, business-critical data) due to insecure handling of Scientist experiment results.
- Privacy Violations and regulatory non-compliance if user data processed by Scientist experiments is leaked.
Risk Severity: High
Mitigation Strategies:
- Data Minimization in Scientist Experiments: Minimize the processing of sensitive data within Scientist experiments whenever possible. Avoid passing sensitive information directly into control or candidate functions if it's not absolutely necessary for the experiment's purpose.
- Access Control for Scientist Experiment Results: Implement strict access controls to restrict access to experiment results and reports generated by Scientist. Ensure only authorized users or systems can view or access this data.
- Data Anonymization/Pseudonymization of Scientist Results: Anonymize or pseudonymize sensitive data in experiment results before they are logged, reported, or exposed through APIs. Process and transform data from Scientist experiments to remove or mask sensitive details before outputting results.
- Secure Transmission and Storage of Scientist Results: Ensure that experiment results from Scientist are transmitted and stored securely, using encryption and secure communication channels, especially if they contain potentially sensitive information.