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Attack Tree Analysis for flame-engine/flame

Objective: Achieve RCE or Game-Specific DoS

Attack Tree Visualization

[Attacker Goal: Achieve RCE or Game-Specific DoS] ├── [1. Achieve RCE] [!] │ ├── [1.1 Exploit Component Lifecycle] │ │ ├── [1.1.1 Manipulate Component Addition/Removal] │ │ │ ├── [1.1.1.1 Inject Malicious Component via Network (if multiplayer)] │ │ │ │ └── [1.1.1.1.1 Craft packet to trigger unexpected component creation with malicious code.] [!] ---> │ │ │ ├── [1.1.1.2 Inject Malicious Component via Save File (if applicable)] │ │ │ │ └── [1.1.1.2.1 Modify save file to include data that instantiates a malicious component on load.] [!] │ │ │ └── [1.1.1.3 Inject Malicious Component via Configuration File (if applicable)] │ │ │ └── [1.1.1.3.1 Modify configuration file to include data that instantiates a malicious component on load.] [!] │ ├── [1.2 Exploit Input Handling] │ │ ├── [1.2.1 Overflow/Underflow in Numerical Input] │ │ │ └── [1.2.1.2 Send excessively large/small values to cause buffer overflows or integer overflows.] [!] │ │ ├── [1.2.2 Injection in String Input] │ │ │ └── [1.2.2.2 Inject code disguised as strings (e.g., Dart code if not properly sanitized).] [!] ---> │ │ └── [1.2.3 Unvalidated Input in Custom Event Handlers] │ │ └── [1.2.3.2 Send malformed event data to trigger unexpected behavior.] [!] ---> │ ├── [1.3 Exploit Resource Loading] │ │ ├── [1.3.1 Path Traversal in Asset Loading] │ │ │ └── [1.3.1.2 Attempt to load assets from outside the intended directory (e.g., "../../etc/passwd").] [!] │ │ └── [1.3.2 Loading Malicious Assets] │ │ ├── [1.3.2.1 Replace legitimate assets with malicious ones (if attacker has write access).] [!] │ │ └── [1.3.2.2 Trick the application into loading assets from a malicious source (e.g., via URL manipulation).] [!] │ └── [1.4 Exploit Third-Party Libraries Used by Flame] │ └── [1.4.2 Research known vulnerabilities in those dependencies.] [!] └── [2. Achieve Game-Specific DoS] [!] ├── [2.1 Resource Exhaustion] │ ├── [2.1.1 Spam Component Creation] │ │ └── [2.1.1.1 Rapidly create many components to consume memory or CPU.] ---> │ ├── [2.1.2 Trigger Excessive Rendering] │ │ └── [2.1.2.1 Force the rendering of a large number of objects or complex effects.] ---> │ └── [2.1.3 Flood Network with Game Events (if multiplayer)] │ └── [2.1.3.1 Send a high volume of game-related packets to overwhelm the server or other clients.] ---> └── [2.3 Exploit Collision Detection] ├── [2.3.1 Create many colliding objects to overload the collision system.] --->

  • Description: In a multiplayer game, the attacker crafts a malicious network packet that, when processed by the server or another client, causes the creation of a Flame component containing malicious Dart code. This code could then be executed, granting the attacker control.
  • Likelihood: Medium
  • Impact: Very High
  • Effort: Medium
  • Skill Level: Advanced
  • Detection Difficulty: Medium
  • Mitigation: Rigorous validation of all network data, especially data related to component creation. Use whitelisting for allowed component types and data structures. Consider network intrusion detection systems.
  • Description: The attacker modifies a game save file to include data that, when loaded by the game, instantiates a malicious Flame component.
  • Likelihood: Low
  • Impact: Very High
  • Effort: Low
  • Skill Level: Intermediate
  • Detection Difficulty: Hard
  • Mitigation: Validate and sanitize all data loaded from save files. Use checksums or digital signatures to verify save file integrity.
  • Description: Similar to save file manipulation, but targets the game's configuration file.
  • Likelihood: Low
  • Impact: Very High
  • Effort: Low
  • Skill Level: Intermediate
  • Detection Difficulty: Hard
  • Mitigation: Validate and sanitize all data loaded from configuration files. Use checksums or digital signatures to verify configuration file integrity. Restrict write access to configuration files.
  • Description: The attacker sends numerical input values that are outside the expected range, potentially causing buffer overflows or integer overflows in Flame components or the underlying Dart code.
  • Likelihood: Low
  • Impact: High
  • Effort: Medium
  • Skill Level: Advanced
  • Detection Difficulty: Medium
  • Mitigation: Implement strict input validation for all numerical inputs, checking for minimum and maximum values. Use safe integer arithmetic operations.
  • Description: The attacker provides string input that contains malicious Dart code. If this input is not properly sanitized, the code could be executed.
  • Likelihood: Low
  • Impact: Very High
  • Effort: Medium
  • Skill Level: Advanced
  • Detection Difficulty: Hard
  • Mitigation: Thoroughly sanitize all string inputs. Use a whitelist of allowed characters where possible. Avoid evaluating user-provided strings as code.
  • Description: The attacker sends specially crafted data to custom event handlers within Flame components, aiming to trigger unexpected behavior or vulnerabilities.
  • Likelihood: Medium
  • Impact: High
  • Effort: Medium
  • Skill Level: Advanced
  • Detection Difficulty: Medium
  • Mitigation: Validate all data passed to custom event handlers. Define strict schemas for event data.
  • Description: The attacker attempts a path traversal attack by providing a manipulated file path to an asset loading function, trying to access files outside the game's designated asset directory.
  • Likelihood: Low
  • Impact: High
  • Effort: Medium
  • Skill Level: Advanced
  • Detection Difficulty: Medium
  • Mitigation: Use Flame's built-in asset loading functions and ensure they properly sanitize file paths. Avoid constructing file paths directly from user input.
  • Description: If the attacker gains write access to the game's asset directory, they could replace legitimate assets with malicious ones.
  • Likelihood: Very Low
  • Impact: Very High
  • Effort: High
  • Skill Level: Advanced
  • Detection Difficulty: Very Hard
  • Mitigation: Strictly control access to the asset directory. Use file integrity monitoring.
  • Description: The attacker manipulates URLs or other input to trick the game into loading assets from a server they control.
  • Likelihood: Low
  • Impact: High
  • Effort: Medium
  • Skill Level: Advanced
  • Detection Difficulty: Medium
  • Mitigation: Validate all URLs used for asset loading. Restrict asset loading to trusted sources.
  • Description: The attacker researches known vulnerabilities in the libraries that Flame depends on.
  • Likelihood: Medium
  • Impact: Variable (Depends on the vulnerability)
  • Effort: Low
  • Skill Level: Intermediate
  • Detection Difficulty: Easy
  • Mitigation: Keep all dependencies up-to-date. Use vulnerability scanning tools.
  • Description: The attacker exploits the game's logic to rapidly create a large number of Flame components, exhausting server or client resources.
  • Likelihood: Medium
  • Impact: Medium
  • Effort: Low
  • Skill Level: Intermediate
  • Detection Difficulty: Easy
  • Mitigation: Implement limits on the number of components that can be created within a given time frame. Monitor resource usage.
  • Description: The attacker triggers the rendering of many objects or complex visual effects, overwhelming the rendering engine.
  • Likelihood: Medium
  • Impact: Medium
  • Effort: Low
  • Skill Level: Intermediate
  • Detection Difficulty: Easy
  • Mitigation: Limit the number of objects that can be rendered simultaneously. Optimize rendering performance. Monitor frame rates.
  • Description: The attacker floods the network with game-related packets, overwhelming the server or other clients.
  • Likelihood: Medium
  • Impact: Medium
  • Effort: Low
  • Skill Level: Intermediate
  • Detection Difficulty: Easy
  • Mitigation: Implement rate limiting on network requests. Use network intrusion detection systems.
  • Description: The attacker creates a large number of colliding objects, overloading Flame's collision detection system.
  • Likelihood: Medium
  • Impact: Medium
  • Effort: Low
  • Skill Level: Intermediate
  • Detection Difficulty: Easy
  • Mitigation: Optimize collision detection algorithms. Use spatial partitioning techniques. Limit the number of colliding objects.