attack-tree.md

Attack Tree Analysis for google/guava

Objective: Compromise application using Guava by exploiting weaknesses or vulnerabilities within Guava or its usage.

Attack Tree Visualization

**Compromise Application Using Guava [CRITICAL]**
├── **Exploit Vulnerabilities in Guava Library Itself [CRITICAL]**
│   ├── **Known Vulnerabilities (CVEs) [CRITICAL]**
│   │   └── 🔍 Search for known CVEs in Guava versions used by the application.
│   ├── **Denial of Service (DoS) via Algorithmic Complexity [CRITICAL]**
│   │   ├── **Hash Collision Attacks on Guava Collections (e.g., HashMaps, HashSets)**
│   │   │   ├── 🎯 Send crafted input to cause hash collisions in Guava's hash-based collections.
│   │   ├── **Resource Exhaustion via Cache Abuse (Guava Caching)**
│   │   │   ├── 🎯 Overwhelm Guava Cache with numerous unique keys to exhaust memory.
│   │   ├── Regular Expression DoS (ReDoS) in String Processing (Guava Strings)
│   │   │   ├── 🎯 Provide crafted input to Guava's string processing functions using vulnerable regular expressions.
│   │   └── Integer Overflow/Underflow in Math Utilities (Guava Math)
│   │       ├── 🎯 Provide large or small inputs to Guava's math functions to cause overflows/underflows leading to unexpected behavior.
│   └── **Vulnerabilities in Third-Party Dependencies of Guava (Transitive Dependencies) [CRITICAL]**
│       └── 🔍 Analyze Guava's dependencies for known vulnerabilities.
├── **Exploit Misuse of Guava by the Application [CRITICAL]**
│   ├── **Insecure Deserialization of Guava Objects [CRITICAL]**
│   │   └── 🎯 If application serializes Guava objects (e.g., `ImmutableList`, `Optional`) and deserializes untrusted data, exploit potential deserialization vulnerabilities.
│   ├── Logic Errors due to Incorrect Guava Usage
│   │   ├── **Incorrect Cache Configuration leading to Data Inconsistency**
│   │   │   ├── 🎯 Exploit weak cache invalidation or eviction policies to cause stale data to be served.
│   │   ├── **Inefficient Algorithms due to Misuse of Guava Collections**
│   │   │   ├── 🎯 Trigger inefficient operations on Guava collections due to incorrect usage patterns, leading to performance degradation.
│   └── **Over-reliance on Guava for Security-Sensitive Operations (Anti-Pattern) [CRITICAL]**
│       └── 🎯 If application incorrectly assumes Guava provides security features it doesn't (e.g., cryptography, authentication), exploit the lack of security in those areas.

Attack Tree Path: 1. Compromise Application Using Guava [CRITICAL]

• Attack Vector Name: Root Goal - Application Compromise via Guava
• Likelihood: High (If vulnerabilities or misuses exist and are not mitigated)
• Impact: High (Full application compromise, data breach, service disruption, RCE)
• Effort: Varies (Low to High depending on specific attack path)
• Skill Level: Varies (Low to Expert depending on specific attack path)
• Detection Difficulty: Varies (Low to High depending on specific attack path)
• Mitigation: Implement all mitigations outlined in the detailed attack tree analysis, prioritize high-risk paths.

Attack Tree Path: 2. Exploit Vulnerabilities in Guava Library Itself [CRITICAL]

• Attack Vector Name: Exploit Guava Library Vulnerabilities
• Likelihood: Medium (Depends on Guava version and existence of known or zero-day vulnerabilities)
• Impact: High (Potentially RCE, DoS, Data Breach, depending on the vulnerability)
• Effort: Low to Medium (Exploits for known CVEs are often readily available, zero-day exploits require more effort)
• Skill Level: Low to Expert (Script kiddie for known CVEs, Expert for zero-day research and exploit development)
• Detection Difficulty: Medium (IDS/IPS might detect known exploit patterns, zero-day exploits are harder to detect)
• Mitigation:
  • Regularly update Guava to the latest stable version with security patches.
  • Monitor security advisories for Guava and its dependencies.
  • Implement robust security monitoring and intrusion detection systems.

Attack Tree Path: 3. Known Vulnerabilities (CVEs) [CRITICAL]

• Attack Vector Name: Exploiting Known CVEs in Guava
• Likelihood: Low to Medium (Depends on the Guava version used by the application and the presence of exploitable CVEs)
• Impact: High (Potentially RCE, DoS, Data Breach, depending on the specific CVE)
• Effort: Low (Exploits for known CVEs are often publicly available)
• Skill Level: Low to Medium (Script kiddie to Intermediate, depending on exploit complexity)
• Detection Difficulty: Medium (IDS/IPS might detect exploit attempts based on known signatures)
• Mitigation:
  • Proactive vulnerability scanning and patching process.
  • Utilize dependency scanning tools to identify vulnerable Guava versions.
  • Rapidly apply security updates provided by the Guava project.

Attack Tree Path: 4. Denial of Service (DoS) via Algorithmic Complexity [CRITICAL]

• Attack Vector Name: Algorithmic Complexity DoS against Guava Collections
• Likelihood: Medium (Feasible in web applications that process user-controlled input using Guava collections)
• Impact: Medium (DoS - CPU exhaustion, application slowdown, service unavailability)
• Effort: Medium (Requires understanding of hashing algorithms and input crafting)
• Skill Level: Medium (Intermediate)
• Detection Difficulty: Medium to High (Distinguishing from legitimate high traffic can be challenging)
• Mitigation:
  • Input validation and sanitization to limit the possibility of crafted inputs.
  • Rate limiting to control the volume of requests.
  • Monitoring CPU usage and request patterns to detect anomalies.
  • Consider using collision-resistant hashing if applicable (though Guava's default hashing is generally robust).

Attack Tree Path: 5. Hash Collision Attacks on Guava Collections (e.g., HashMaps, HashSets)

• Attack Vector Name: Hash Collision DoS on Guava Collections
• Likelihood: Medium (If application uses Guava hash-based collections to process external input)
• Impact: Medium (DoS - CPU exhaustion, service slowdown)
• Effort: Medium (Requires understanding of hash collision principles and crafting malicious input)
• Skill Level: Medium (Intermediate)
• Detection Difficulty: Medium to High (Difficult to differentiate from legitimate high load, requires deep traffic analysis)
• Mitigation:
  • Input validation to restrict input size and complexity.
  • Rate limiting requests.
  • Monitor CPU utilization and request latency for unusual spikes.

Attack Tree Path: 6. Resource Exhaustion via Cache Abuse (Guava Caching)

• Attack Vector Name: Cache Abuse leading to Resource Exhaustion
• Likelihood: Medium (If Guava Cache is exposed to external input and lacks proper limits)
• Impact: Medium to High (Memory exhaustion, application crash, service unavailability)
• Effort: Low to Medium (Simple scripting to generate unique cache keys)
• Skill Level: Low to Medium (Novice to Intermediate)
• Detection Difficulty: Medium (Monitor memory usage and cache hit/miss ratios)
• Mitigation:
  • Configure Guava Cache with appropriate size limits and eviction policies.
  • Implement rate limiting on cache population to prevent rapid key insertion.
  • Monitor memory usage and cache performance metrics.

Attack Tree Path: 7. Vulnerabilities in Third-Party Dependencies of Guava (Transitive Dependencies) [CRITICAL]

• Attack Vector Name: Transitive Dependency Vulnerabilities
• Likelihood: Low to Medium (Dependencies can contain vulnerabilities, requiring regular scanning)
• Impact: High (Depends on the vulnerability, potentially RCE, DoS, Data Breach)
• Effort: Low (Dependency scanning tools automate vulnerability identification)
• Skill Level: Low (Using dependency scanning tools is straightforward)
• Detection Difficulty: Low (Dependency scanning tools readily identify known vulnerabilities)
• Mitigation:
  • Regularly scan application dependencies, including transitive dependencies of Guava, for known vulnerabilities.
  • Use dependency scanning tools integrated into the development pipeline.
  • Update Guava and its dependencies promptly to address identified vulnerabilities.

Attack Tree Path: 8. Exploit Misuse of Guava by the Application [CRITICAL]

• Attack Vector Name: Application Misuse of Guava Library
• Likelihood: Medium (Developers might misuse library features or not fully understand security implications)
• Impact: Medium to High (Data inconsistency, DoS, Information Disclosure, potentially RCE in specific misuse scenarios like deserialization)
• Effort: Varies (Low to High depending on the specific misuse)
• Skill Level: Low to Medium (Novice to Intermediate, Expert for complex misuse scenarios)
• Detection Difficulty: Medium to High (Logic errors and misuse can be subtle and hard to detect through automated means)
• Mitigation:
  • Thorough code reviews focusing on Guava usage patterns.
  • Security testing and penetration testing to identify logic flaws and misconfigurations.
  • Developer training on secure coding practices and proper Guava usage.

Attack Tree Path: 9. Insecure Deserialization of Guava Objects [CRITICAL]

• Attack Vector Name: Insecure Deserialization of Guava Objects
• Likelihood: Low (Less common to directly serialize Guava objects in a vulnerable manner, more of a general deserialization risk if application uses serialization)
• Impact: High (Remote Code Execution - RCE)
• Effort: Medium to High (Requires understanding of deserialization vulnerabilities and crafting exploits)
• Skill Level: Medium to High (Intermediate to Expert)
• Detection Difficulty: Medium to High (Can be stealthy, requires deep traffic inspection and code analysis)
• Mitigation:
  • Avoid deserializing untrusted data whenever possible.
  • If deserialization is necessary, use secure deserialization practices and libraries.
  • Consider not serializing Guava objects directly if alternatives exist.

Attack Tree Path: 10. Incorrect Cache Configuration leading to Data Inconsistency

• Attack Vector Name: Cache Misconfiguration leading to Data Inconsistency
• Likelihood: Medium (Common application logic flaw, especially with caching implementations)
• Impact: Medium (Data corruption, incorrect application state, business logic bypass)
• Effort: Low to Medium (Requires understanding of application logic and cache behavior)
• Skill Level: Low to Medium (Novice to Intermediate)
• Detection Difficulty: Medium to High (Data inconsistency can be subtle, requires functional testing and business logic validation)
• Mitigation:
  • Carefully design cache invalidation and eviction strategies.
  • Ensure cache consistency with underlying data sources through proper synchronization mechanisms.
  • Implement thorough functional testing to verify cache behavior and data consistency.

Attack Tree Path: 11. Inefficient Algorithms due to Misuse of Guava Collections

• Attack Vector Name: Performance Degradation via Inefficient Guava Collection Usage
• Likelihood: Medium (Developers might not always be aware of performance implications of collection operations)
• Impact: Medium (Application slowdown, DoS, resource exhaustion)
• Effort: Low to Medium (Requires understanding of collection performance and application usage)
• Skill Level: Medium (Intermediate)
• Detection Difficulty: Medium (Performance monitoring, slow transaction tracing can reveal inefficient operations)
• Mitigation:
  • Understand the performance characteristics of different Guava collection types.
  • Choose appropriate collections based on usage patterns and performance requirements.
  • Optimize collection operations and avoid inefficient patterns.
  • Conduct performance testing and profiling to identify bottlenecks.

Attack Tree Path: 12. Over-reliance on Guava for Security-Sensitive Operations (Anti-Pattern) [CRITICAL]

• Attack Vector Name: Misplaced Security Reliance on Guava
• Likelihood: Low (Developers generally understand Guava's purpose, but misinterpretations can occur)
• Impact: High (Security vulnerabilities due to weak or missing security measures)
• Effort: Low to Medium (Exploiting weak security measures is often relatively easy)
• Skill Level: Low to Medium (Novice to Intermediate, depending on the specific vulnerability)
• Detection Difficulty: Medium to High (Security flaws due to missing security measures can be hard to detect without security audits)
• Mitigation:
  • Clearly define security responsibilities and boundaries for libraries used in the application.
  • Do not rely on Guava for security-sensitive operations like cryptography, authentication, or authorization.
  • Use dedicated and well-vetted security libraries for security-critical functionalities.
  • Conduct security audits and penetration testing to identify potential security gaps.