From Naive Execution to Safe Isolation: Building a Self-Hosted Code Runtime

The challenge of executing user-provided code represents one of the fundamental problems in distributed systems development. When building dev tools, automation platforms, or systems with plugins and AI-generated logic, developers inevitably face the critical decision of how to safely execute code that originates from untrusted sources.

The Problem: Why Code Execution is Hard

The naive approach appears straightforward: execute the user code within the application context and return the result. This method works until someone uploads code that contains an infinite loop, consumes excessive memory, accesses unauthorized files, abuses network resources, or simply crashes the process. The reality is that executing code is technically simple; executing it safely represents a significant architectural challenge.

The core problem stems from the fundamental tension between functionality and security. User-provided code needs execution resources, yet those same resources can be weaponized to compromise the host system. This creates a classic distributed systems dilemma: how to provide computational capabilities while maintaining strong isolation guarantees.

Security considerations extend beyond simple containment. The execution environment must prevent side-channel attacks, where code infers information about other processes or the host system through timing differences, power consumption, or other indirect methods. This requires careful hardware-level isolation and potentially specialized security-focused container runtimes.

The Solution Approach: Building an Isolated Execution Environment

The solution requires a multi-layered approach that begins with process isolation. Container technologies like Docker provide a foundation for creating execution environments that are separate from the host system. However, containers alone are insufficient for robust code execution. They must be combined with resource limits, filesystem restrictions, and network controls to create a comprehensive security model.

Resource Constraints

Resource constraints represent a critical component of safe execution. CPU time limits prevent infinite loops from consuming system resources indefinitely. Memory limits contain memory bombs that could otherwise crash the host. Execution timeouts ensure that processes eventually terminate, even if they're not explicitly CPU-bound. These constraints must be enforced at the container level, with monitoring processes that can terminate violators without affecting other executions.

In practice, this implementation typically involves Linux cgroups for resource control. Cgroups (control groups) provide a kernel-level mechanism for limiting, accounting, and isolating the resource usage of collections of processes. They allow precise control over CPU shares, memory limits, I/O bandwidth, and other resources, forming the foundation for container security.

Filesystem Isolation

Filesystem isolation presents another challenge. The execution environment needs access to necessary resources while preventing access to sensitive system files. This typically involves creating a minimal read-only filesystem for the execution environment, with temporary write access to designated directories. The mount namespace in Linux containers provides this capability, allowing fine-grained control over visible filesystems.

The implementation often involves layered filesystems like OverlayFS, which enable efficient creation of container-specific filesystem layers on top of a base image. This approach minimizes storage overhead while maintaining isolation, as each container sees only its own filesystem view.

Network Isolation

Network isolation requires similar careful configuration. Execution environments should have limited network access, typically restricted to specific ports or entirely blocked, depending on the use case. Network namespaces in containers enable this isolation, ensuring that malicious code cannot communicate with unauthorized services or consume excessive bandwidth.

For added security, the runtime can implement egress filtering, controlling which external services containers can communicate with. This prevents data exfiltration and limits the potential damage from compromised execution environments. The implementation often involves iptables rules or more advanced network policies.

State Management and Cleanup

State management introduces additional complexity. Each execution represents a separate state that must be properly initialized, executed, and cleaned up. The system must handle failures gracefully, ensuring that resources are properly released even when code crashes or violates constraints. This requires robust error handling and cleanup mechanisms that can operate independently of the executed code.

The runtime should implement a proper execution lifecycle: initialization, execution, and cleanup. During initialization, the environment is prepared with necessary dependencies and resource limits. During execution, the code runs with enforced constraints. During cleanup, all temporary files, processes, and network connections are terminated and removed.

Trade-offs: Balancing Security and Functionality

The implementation of these systems reveals several important trade-offs. Performance versus security represents the most significant trade-off. Stronger isolation typically comes with performance overhead as additional checks and barriers are introduced. The execution runtime must balance these competing requirements based on the specific use case.

Container runtimes like Docker provide good isolation but add significant overhead compared to process isolation alone. For higher security requirements, solutions like gVisor or Kata Containers offer stronger isolation with less performance impact than traditional containers but more than process isolation.

Another important trade-off exists between flexibility and control. More permissive execution environments allow for greater functionality but increase the risk of security breaches. The runtime must define clear boundaries that provide sufficient flexibility for legitimate use cases while preventing abuse.

For example, allowing filesystem access to specific directories enables useful functionality but requires careful implementation to prevent symlink attacks or path traversal vulnerabilities. The runtime must validate all paths and ensure they remain within designated boundaries.

Scalability Considerations

Scalability considerations further complicate the design. As the number of concurrent executions increases, the system must maintain isolation guarantees without becoming a bottleneck. This requires efficient resource management, potentially including technologies like Kubernetes for orchestration at scale.

The runtime should implement proper resource pooling and reuse to minimize overhead. This includes reusing container images, caching filesystem layers, and maintaining a pool of pre-initialized execution environments that can be quickly repurposed for new executions.

Self-hosted vs SaaS

The choice between self-hosted and SaaS solutions involves additional trade-offs. Self-hosted solutions provide greater control and avoid vendor lock-in, but require operational overhead. SaaS solutions offer convenience and built-in security expertise, but come with subscription costs and potential limitations on customization.

Self-hosted solutions require investment in infrastructure, security expertise, and operational processes. However, they offer complete control over the execution environment, allowing customization to specific requirements and integration with existing systems. For organizations with strong DevOps capabilities and specific compliance requirements, self-hosted solutions often provide the best balance of control and cost.

Technical Implementation Challenges

When implementing such a system, several technical challenges emerge. Resource enforcement must be reliable and tamper-proof. The runtime cannot trust the executed code to respect its own limits, requiring external monitoring and enforcement mechanisms. This typically involves cgroups on Linux systems for resource control, combined with monitoring processes that can intervene when limits are exceeded.

Deterministic execution represents another challenge. The system must ensure that results are consistent across multiple executions with the same inputs, even in the presence of concurrent executions. This requires careful management of execution environments and resource allocation to prevent interference between different executions.

Error handling and reporting must be comprehensive yet secure. The system must communicate failures to the calling application without exposing sensitive information about the host environment. This involves careful filtering of error messages and secure communication channels between the execution environment and the calling application.

Architectural Patterns

For developers considering such a system, several patterns emerge. The microservices approach, where the execution runtime operates as a separate service, provides clear boundaries and scalability. The event-driven architecture, where executions are queued and processed asynchronously, improves responsiveness and load balancing.

The microservices approach allows the execution runtime to scale independently of the main application. This separation enables better resource utilization, as the execution service can be scaled based on demand without affecting other application components. The event-driven pattern, using message queues like RabbitMQ or Kafka, provides additional resilience and decoupling.

Technology Stack Considerations

The choice of technology stack significantly impacts the system's characteristics. Container runtimes like Docker or containerd provide isolation, but add overhead. Lightweight runtimes like Kata Containers or gVisor offer stronger security with less performance impact. The specific requirements of the application will determine the optimal technology choice.

For higher security requirements, unikernels represent an interesting alternative. Unikernels compile application code directly with a minimal operating system, creating a single address space that can be more securely isolated than traditional containers. However, they come with their own limitations, including reduced flexibility and potentially longer startup times.

Monitoring and Observability

Monitoring and observability become essential components of a production execution system. The runtime must provide metrics on execution times, resource usage, failure rates, and other key indicators. This enables operators to identify and address performance bottlenecks, security vulnerabilities, and operational issues.

The monitoring system should track both the execution environment itself and the performance of the code being executed. This includes metrics like container creation time, memory usage, CPU utilization, execution duration, and error rates. These metrics can be collected using tools like Prometheus and visualized with Grafana.

Conclusion

Building a self-hosted code execution runtime requires careful consideration of multiple factors. The system must balance security, performance, and scalability while providing the functionality needed for the specific use case. The isolation techniques and resource controls discussed here provide a foundation for building robust execution environments that can safely run untrusted code.

For developers interested in implementing such a system, the GozoLite project offers an example of a lightweight, self-hosted execution runtime. The project demonstrates many of the principles discussed here, providing a practical starting point for building custom solutions tailored to specific requirements.