How to sandbox AI agents in 2026: MicroVMs, gVisor & isolation strategies

AI agents are autonomous systems that generate and execute code, call APIs, access data, and make decisions without human oversight.

Unlike traditional applications, where developers write and review every line of code, AI agents produce code dynamically based on prompts, context, and objectives. This creates fundamental security challenges:

• AI agents generate code you haven't reviewed or audited
• Prompt injection attacks manipulate agent behavior to execute malicious actions
• Compromised agents abuse APIs and system access beyond intended scope
• Successful exploits enable data exfiltration and lateral movement across infrastructure
• Agents can become rogue insiders with programmatic access to critical systems

When 83% of companies plan to deploy AI agents, understanding sandboxing becomes essential for preventing security breaches that traditional cybersecurity tools weren't designed to handle.

AI agent sandboxing creates isolated execution environments where agents can run code without affecting the host system or other workloads.

A sandbox provides strict boundaries that limit what an agent can access, modify, or interact with. Effective sandboxing addresses multiple threat vectors simultaneously: code execution exploits, filesystem access, network communication, resource consumption, and privilege escalation.

The security model operates on zero-trust principles where all agent actions are explicitly allowed rather than implicitly permitted, treating all AI-generated code as potentially malicious.

Different isolation technologies provide different security guarantees and performance characteristics for AI agent workloads.

Docker containers use Linux namespaces and cgroups to isolate processes while sharing the host kernel.

• Security model: Containers rely on kernel features for isolation. A kernel vulnerability or misconfiguration can allow container escape, giving attackers host access.
• Performance: Fast startup (milliseconds), minimal overhead, high density.
• Use case: Suitable only for trusted, vetted code in single-tenant environments.

gVisor implements a user-space kernel that intercepts system calls before they reach the host kernel.

When a container makes a syscall, gVisor's Sentry process handles it in user space, drastically reducing kernel attack surface. Instead of hundreds of syscalls reaching the host kernel, gVisor allows only a minimal, vetted subset.

• Security model: Syscall-level isolation. Stronger than containers, weaker than VMs.
• Performance: Some overhead on I/O-heavy workloads (10-30%), fast startup.
• Use case: Compute-heavy AI workloads where full VM isolation isn't justified.

Firecracker creates lightweight virtual machines with minimal device emulation, running each microVM with its own Linux kernel inside KVM.

• Security model: Hardware-level isolation. Each workload has a dedicated kernel completely separated from the host. Attackers must escape both the guest kernel and the hypervisor.
• Performance: Boots in ~125ms, less than 5 MiB overhead per VM, up to 150 VMs per second per host.
• Use case: Multi-tenant AI agent execution, untrusted code, production environments.

Kata Containers orchestrates multiple VMMs (Firecracker, Cloud Hypervisor, QEMU) to provide microVM isolation through standard container APIs.

It integrates with Kubernetes, handling all operational complexity of running microVMs. From Kubernetes' perspective, it's a normal container. Under the hood, it's a full VM with hardware isolation.

• Security model: Same hardware-level isolation as Firecracker, with Kubernetes-native orchestration.
• Performance: Boots in ~200ms, minimal memory overhead.
• Use case: Production Kubernetes workloads needing VM-level security with container workflows.

See the following related articles:

• Firecracker vs gVisor: Which isolation technology should you use?
• Kata Containers vs Firecracker vs gVisor: Which container isolation tool should you use?

The right isolation technology depends on your threat model and workload characteristics. See the table below that summarizes the answer:

In a nutshell:

• For production AI agents executing untrusted code: Use Firecracker microVMs or Kata Containers. The hardware boundary prevents entire classes of kernel-based attacks.
• For compute-heavy agents with limited I/O: gVisor provides strong isolation without full VM overhead.
• For trusted internal automation: Hardened containers with seccomp, AppArmor, and capability dropping work only when agents execute code you've reviewed and trust.

AI agents can consume excessive resources either accidentally or maliciously, requiring strict limits on CPU, memory, disk, and network usage.

• CPU limits: Prevent compute exhaustion by setting maximum CPU shares and throttling runaway processes.
• Memory limits: Stop memory bombs by defining hard limits that terminate processes exceeding allocation.
• Disk quotas: Block storage attacks by limiting filesystem usage and rate-limiting I/O operations.
• Network bandwidth: Prevent data exfiltration by rate-limiting outbound traffic and monitoring for unusual patterns.

AI agents should operate on a zero-trust network model where all connections are explicitly allowed rather than implicitly permitted.

• Egress filtering: Block all outbound connections by default. Whitelist only required API endpoints and services.
• DNS restrictions: Limit DNS resolution to prevent discovery attacks and command-and-control communication.
• Network segmentation: Isolate agent networks from production systems and sensitive data stores.

Grant AI agents only the minimum permissions required for their specific tasks, following the principle of least privilege.

• Short-lived credentials: Issue temporary tokens with limited scope for each task. Expired credentials can't be reused if compromised.
• Tool-specific permissions: Different agent capabilities require different permission sets. Separate read-only from write access.
• Human-in-the-loop gates: Require explicit human approval for high-risk actions like financial transactions or data deletion.

Comprehensive logging and monitoring detect compromised agents before they cause damage.

• Execution tracking: Log all code execution attempts, tool calls, and API requests with immutable audit trails.
• Anomaly detection: Monitor for unexpected network connections, excessive API calls, and unusual resource consumption.
• Failed access attempts: Track permission denials and policy violations as indicators of compromise.

Understanding attack vectors helps you design better sandboxes for AI agent workloads.

• Prompt injection attacks: Attackers craft inputs that manipulate agent behavior, causing it to execute malicious actions or leak data. Mitigate with input validation, prompt filtering, output monitoring, and sandboxed tool execution.
• Code generation exploits: Agents generate code containing vulnerabilities or malicious logic. Mitigate with code execution sandboxing in isolated containers with no network access and minimal system privileges.
• Context poisoning: Attackers modify information agents rely on for continuity (dialog history, RAG knowledge bases), warping future reasoning. Mitigate with cryptographic verification of context data and immutable storage.
• Tool abuse: Agents misuse available tools with dangerous parameters. Mitigate with policy enforcement gates that vet agent plans before execution and human approval for critical operations.

Most teams face a choice between building custom sandbox infrastructure or using an existing platform.

Building your own gives full control over security policies but requires significant engineering investment (months of work), ongoing operational burden for patching and scaling, and expertise in virtualization, networking, and Kubernetes.

Using a platform provides production-ready infrastructure immediately, abstracts operational complexity, handles regular security updates and compliance, and lets engineering resources focus on agent capabilities rather than infrastructure.

Platforms like Northflank provide both Kata Containers and gVisor, choosing appropriate isolation based on workload requirements while processing isolated workloads at scale with automatic security hardening built in.

Northflank provides a secure runtime environment by default, isolating every container in the way that makes sense for your workload.

Infrastructure-adaptive isolation:

• On infrastructure where nested virtualization is available: Northflank runs Kata Containers with Cloud Hypervisor for hardware-level isolation
• On environments where nested virtualization is unavailable: Northflank uses gVisor for syscall-level isolation

This becomes critical when you're working with AI agents that demand API tokens or environment variables. They might need your Cloudflare auth token, your Stripe secret key, or Postgres access. Without proper isolation, you're giving them the ability to become your infrastructure.

Enterprise customers run secure multi-tenant AI agent deployments processing thousands of code executions daily. The platform handles kernel image management, networking configuration, security hardening, and orchestration complexity automatically. You get VM-grade security with container-grade workflows on any cloud.

Try Northflank to sandbox your AI agents with production-ready infrastructure, or talk to an engineer about your specific isolation requirements.

Follow these practices when deploying AI agents in production environments.

• Start with strong isolation: Default to microVMs for untrusted code. Relax to gVisor or containers only when threat model justifies it.
• Implement defense-in-depth: Combine multiple security layers including sandboxing, monitoring, approval gates, and signed artifacts.
• Limit agent scope: Start with narrow, well-defined tasks where the blast radius of failures is contained. Expand capabilities gradually.
• Validate failure modes: Test what happens when agents behave maliciously. Can they delete files, exfiltrate data, or escalate privileges?
• Monitor continuously: Log all agent actions, tool calls, and resource usage. Set alerts for policy violations and anomalous behavior.
• Plan for rapid change: Best practices evolve monthly as new attack techniques emerge. What's adequate protection today may be insufficient next quarter.

Containerization provides process-level isolation using Linux namespaces and cgroups. Sandboxing is a broader concept that includes containers but also encompasses stronger isolation technologies like microVMs and user-space kernels. For AI agents, standard containers alone don't provide sufficient isolation because they share the host kernel.

Docker containers share the host kernel with all other containers. A kernel vulnerability or misconfiguration can allow container escape, giving attackers access to the host and other containers. AI agents generate unpredictable code that might exploit these vulnerabilities. MicroVMs provide dedicated kernels per workload, eliminating this entire attack vector.

The overhead depends on the isolation technology. Firecracker microVMs boot in ~125ms with less than 5 MiB memory overhead. gVisor adds 10-30% overhead on I/O-heavy workloads but minimal overhead on compute-heavy tasks. For most AI agent workloads, the security benefits far outweigh the performance cost.

For production environments running untrusted AI-generated code, Firecracker microVMs or Kata Containers provide the strongest isolation. They create hardware-enforced boundaries that prevent kernel-based exploits. gVisor is acceptable for compute workloads where you control the code. Standard containers are insufficient for untrusted code.

Use Kata Containers with a RuntimeClass that specifies the kata-clh handler. Kata integrates with Kubernetes through CRI, automatically provisioning microVMs for pods that specify the Kata RuntimeClass. This provides VM-level isolation with standard Kubernetes workflows and APIs.

Most teams are better served using existing platforms rather than building custom infrastructure. Building sandbox infrastructure requires months of engineering work and ongoing operational burden. Platforms like Northflank provide production-ready sandbox infrastructure with Kata Containers and gVisor, handling all operational complexity so you can focus on agent capabilities.