threat-modeling.md

Threat Model Analysis for google/guava

Threat: Known Vulnerability Exploitation

• Description: An attacker exploits a publicly known vulnerability in a specific version of Guava. They might use readily available exploit code to target applications using vulnerable Guava versions by sending crafted requests or data that triggers the vulnerability.
• Impact: Depending on the vulnerability, impact could include Remote Code Execution (RCE), potentially leading to full system compromise, or significant Information Disclosure.
• Guava Component Affected: Varies depending on the specific CVE. Could affect any Guava module (e.g., Hashing, Collections, Cache, etc.).
• Risk Severity: Critical
• Mitigation Strategies:
  • Keep Guava updated: Immediately update to the latest stable Guava version to patch known vulnerabilities upon release of security updates.
  • Vulnerability Scanning: Implement automated dependency vulnerability scanning in the CI/CD pipeline to detect outdated and vulnerable Guava versions.
  • Security Monitoring: Subscribe to security advisories and CVE databases related to Guava to be informed of new vulnerabilities.

Threat: Zero-Day Vulnerability Exploitation

• Description: An attacker discovers and exploits an unknown vulnerability in Guava before a patch is available. They might reverse engineer Guava code or use fuzzing techniques to find vulnerabilities. Exploitation methods are similar to known vulnerabilities but without readily available public information, making detection harder.
• Impact: Similar to known vulnerabilities, impact can include Remote Code Execution (RCE), potentially leading to full system compromise, or significant Information Disclosure.
• Guava Component Affected: Potentially any Guava module.
• Risk Severity: Critical
• Mitigation Strategies:
  • Defense in Depth: Implement strong general security practices (input validation, output encoding, least privilege, security audits, etc.) to limit the potential impact of any library vulnerability, including zero-days.
  • Web Application Firewall (WAF): Use a WAF to detect and block suspicious traffic patterns that might indicate exploitation attempts, even for unknown vulnerabilities.
  • Incident Response Plan: Maintain a robust incident response plan to quickly react to and mitigate security incidents, including potential zero-day exploits.
  • Regular Security Audits & Penetration Testing: Conduct periodic security audits and penetration testing to proactively identify potential weaknesses that could be exploited in conjunction with a zero-day.

Threat: Outdated Guava Version Usage

• Description: Developers fail to update Guava, continuing to use an older version with known, patched vulnerabilities. Attackers can easily identify applications using outdated Guava versions (e.g., through dependency scanning or version disclosure in error messages or public resources) and exploit the publicly known vulnerabilities.
• Impact: Exposure to known vulnerabilities, potentially leading to Remote Code Execution (RCE), significant Information Disclosure, or Denial of Service (DoS), depending on the specific vulnerability in the outdated version.
• Guava Component Affected: Varies depending on the vulnerability in the outdated version.
• Risk Severity: High to Critical (Severity depends on the specific vulnerability present in the outdated version).
• Mitigation Strategies:
  • Dependency Management: Utilize a robust dependency management system (Maven, Gradle) to strictly manage and track Guava versions.
  • Automated Updates: Implement automated dependency updates as a critical part of the CI/CD pipeline to ensure timely patching.
  • Regular Dependency Review: Regularly review and proactively update dependencies, including Guava, to the latest stable versions, even outside of automated updates, to catch up with security releases.

Threat: Dependency Confusion (Unlikely for Guava, but theoretically possible)

• Description: An attacker attempts to introduce a malicious library with a similar name to Guava into the application's dependencies. They might register a package with a name closely resembling "com.google.guava" in a public repository, hoping the dependency management system will mistakenly download and include the malicious package instead of the legitimate Guava library.
• Impact: If successful, the attacker's malicious code would be included in the application build and runtime, potentially allowing for arbitrary malicious actions, including Remote Code Execution (RCE), data theft, creation of backdoors, or complete system compromise.
• Guava Component Affected: Potentially replaces the entire Guava library, effectively affecting all components and application functionality relying on Guava.
• Risk Severity: Critical
• Mitigation Strategies:
  • Trusted Repositories: Strictly configure and use only trusted and highly reputable dependency repositories (e.g., Maven Central) and enforce this in build configurations.
  • Dependency Verification: Implement and enforce dependency verification mechanisms, such as checksum verification and signature verification (if available), to ensure downloaded dependencies are authentic and untampered with.
  • Repository Configuration Lockdown: Carefully configure dependency repositories and explicitly define trusted sources, preventing accidental or malicious inclusion of dependencies from untrusted locations.
  • Code Review & Build Audits: Regularly review dependency lists and build configurations to detect any unexpected, suspicious, or look-alike dependencies that might indicate a dependency confusion attack.

Threat: DoS via Inefficient Guava Usage (Under Specific Circumstances)

• Description: Developers utilize resource-intensive Guava utilities (e.g., complex hashing algorithms, inefficient collection operations, poorly configured caching) in a way that can be exploited by an attacker to cause a Denial of Service. An attacker might send specially crafted inputs or a large volume of requests specifically designed to trigger these inefficient operations, leading to excessive server resource consumption (CPU, memory, network) and rendering the application unresponsive or significantly degraded.
• Impact: Denial of Service (DoS), making the application unavailable or severely degraded for legitimate users, potentially causing significant business disruption and reputational damage.
• Guava Component Affected: Potentially various Guava modules, particularly Hashing, Collections, Cache, and Strings if used inefficiently or without proper resource management.
• Risk Severity: High (Can be downgraded to Medium if the DoS impact is limited and easily mitigated, but in scenarios with critical applications and easily exploitable inefficient usage, it remains High).
• Mitigation Strategies:
  • Performance Testing & Profiling: Conduct rigorous performance testing and profiling of application components that utilize Guava, specifically focusing on resource consumption under various load conditions and with potentially malicious inputs.
  • Code Optimization & Resource Management: Optimize code that utilizes Guava libraries for efficiency, paying close attention to resource usage, especially in performance-critical paths and areas handling external input. Implement resource management techniques like connection pooling, thread pooling, and memory limits.
  • Input Validation, Sanitization & Rate Limiting: Implement robust input validation and sanitization to prevent processing of excessively large, deeply nested, or maliciously crafted inputs that could trigger resource-intensive Guava operations. Implement rate limiting and request quotas to protect against volumetric DoS attacks exploiting inefficient Guava usage.
  • Monitoring & Alerting: Implement comprehensive monitoring of application performance and resource utilization, with alerting mechanisms to quickly detect and respond to unusual resource consumption patterns that might indicate a DoS attack exploiting Guava inefficiencies.