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attack-surface.md

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Attack Surface Analysis for github/scientist

Attack Surface: 1. Unintended Side Effects in Candidate Code

  • Description: The candidate (new) code path, even if intended for observation, contains a vulnerability that causes unintended state changes or external interactions.
  • Scientist Contribution: Scientist executes the candidate code path alongside the control path, making any vulnerability in the candidate code directly exploitable in a production environment. This is the fundamental and most critical risk of using Scientist.
  • Example: A candidate code path intended to read user data has a SQL injection vulnerability. Scientist executes this vulnerable code, allowing an attacker to modify or delete data.
  • Impact: Data corruption, data loss, unauthorized access, system compromise.
  • Risk Severity: Critical
  • Mitigation Strategies:
    • Rigorous Code Review: Perform extremely thorough code reviews of all candidate code, focusing on security vulnerabilities. This is the most important mitigation.
    • Input Validation & Output Encoding: Implement strict input validation and output encoding in the candidate code, even if it's believed to be read-only.
    • Sandboxing/Isolation: If possible, execute the candidate code in a sandboxed environment with limited privileges. Consider database transactions that are always rolled back.
    • Principle of Least Privilege: Ensure the candidate code runs with the absolute minimum necessary permissions.
    • Static Analysis: Use static analysis tools to automatically detect potential vulnerabilities.

Attack Surface: 2. Denial of Service (DoS) via Candidate Code

  • Description: The candidate code path is significantly slower or more resource-intensive than the control path, leading to performance degradation or resource exhaustion.
  • Scientist Contribution: Scientist's parallel execution of both code paths means that a slow or resource-hungry candidate code path directly impacts the overall application performance. An attacker could intentionally trigger this.
  • Example: An attacker crafts input that triggers a computationally expensive operation (e.g., a complex regular expression or a large database query) in the candidate code, while the control code handles it efficiently.
  • Impact: Application slowdown, unavailability, denial of service.
  • Risk Severity: High
  • Mitigation Strategies:
    • Resource Limits & Timeouts: Implement strict resource limits (CPU, memory) and timeouts specifically for the candidate code execution within the Scientist experiment.
    • Performance Monitoring: Closely monitor the performance of both paths. Set up alerts for significant discrepancies.
    • Circuit Breakers: Implement circuit breakers to automatically disable experiments that consistently exceed resource thresholds.
    • Load Testing: Perform load testing that specifically targets the Scientist experiment.
    • Rate Limiting: Apply rate limiting to prevent attackers from overwhelming the system.

Attack Surface: 3. Sensitive Data Leakage in Mismatches

  • Description: Differences between the control and candidate code results, which are logged or published by Scientist, contain sensitive information.
  • Scientist Contribution: Scientist's core function is to compare results and report mismatches. This reporting is the potential leak.
  • Example: A mismatch log contains the full, unredacted results from both code paths, including PII or partially hashed passwords.
  • Impact: Exposure of personally identifiable information (PII), credentials, or other sensitive data.
  • Risk Severity: High
  • Mitigation Strategies:
    • Custom Comparison Function: Implement a custom comparison function that sanitizes or redacts sensitive information before logging/publishing. Never log raw results.
    • Data Minimization: Compare only the essential parts of the results.
    • Review Publisher Configuration: Ensure the publisher handles sensitive data appropriately (encryption, access controls).
    • Data Loss Prevention (DLP): Consider DLP tools to monitor and prevent leakage.

Attack Surface: 4. Data Exfiltration via Publisher

  • Description: The configured publisher for Scientist results is compromised or misconfigured, allowing an attacker to access the comparison data.
  • Scientist Contribution: Scientist relies on the publisher to report results. The publisher's security is paramount.
  • Example: Scientist publishes to an unauthenticated, publicly accessible endpoint, or a logging service with weak access controls.
  • Impact: Exposure of comparison data (potentially including sensitive information) to unauthorized parties.
  • Risk Severity: High
  • Mitigation Strategies:
    • Secure Communication: Use HTTPS for publishing.
    • Authentication & Authorization: Implement strong authentication and authorization for the publisher.
    • Regular Security Audits: Audit the publisher's security regularly.
    • Encryption: Consider encrypting the published data.
    • Principle of Least Privilege: Grant the publisher only minimum necessary permissions.

Attack Surface: 5. Injection Attacks in Custom Comparators/Publishers

  • Description: Vulnerabilities in custom comparators or publishers allow for injection attacks.
  • Scientist Contribution: Scientist allows for custom comparators and publishers, which become part of the attack surface.
  • Example: A custom comparator concatenates strings from results without sanitization, leading to string injection.
  • Impact: Code execution, data corruption, denial of service (depending on the vulnerability).
  • Risk Severity: High
  • Mitigation Strategies:
    • Code Review: Thoroughly review and test custom comparators/publishers for vulnerabilities.
    • Input Validation/Sanitization: Implement robust input validation/sanitization.
    • Secure Coding Practices: Follow secure coding practices.