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Use layered isolation, not a single setting. Run each untrusted agent in a microVM or comparably strong sandbox, deny outbound network access by default, expose only explicitly approved files and services, and deliver credentials through a broker rather than placing secrets in the guest. If agents share a cache, treat the cache as a tenant boundary: derive tenant identity from authenticated server context, use collision-resistant key namespaces and ACLs, and disable commands that can address the whole keyspace. Move to dedicated cache, inference, nodes, or hosts when shared process, memory, timing, latency, lifecycle, or compliance requirements exceed what logical controls can provide.
The right design depends on whether agents have the same owner and trust level, whether prompts can cause code execution, what data may cross a boundary, and whether a hardware side channel is in scope.
What does “isolation” mean when agents share a host?
Isolation is a set of boundaries, not a product label. An agent can be separated from another agent’s files yet still share a kernel, network path, credential, cache process, CPU cache, or noisy physical host. Each shared resource is a possible data path, attack surface, or performance dependency.
OpenAI’s security guidance recommends isolated compute such as virtual machines and separate environments for workloads that must not share data. Its security engineering work also describes agents in separate virtual machines exchanging synthetic secrets through processor-cache timing. That result matters even when the VMs have no network connection and no shared writable directory: shared hardware can remain a covert channel for a sufficiently capable adversary.
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Start by writing down the threat model:
- Trust: Are all agents operated by one team, or can different users submit untrusted prompts and code?
- Data: May an agent read another tenant’s source, conversation history, embeddings, model outputs, or credentials?
- Control plane: Could an agent reach the host, container runtime, orchestration API, metadata service, or local management socket?
- Side channels: Must you defend against timing, cache-eviction, or resource-contention inference?
- Operations: Do tenants need independent deletion, migration, upgrades, backups, or incident response?
A same-owner batch of low-sensitivity agents can often use a hardened process or container sandbox. Different users, prompt-driven code execution, or regulated data generally justify a microVM or a dedicated sandbox per agent. Mutually adversarial tenants, strict latency guarantees, or compliance boundaries may require dedicated hosts, clusters, cache instances, or inference instances.
Which boundary should I choose: microVM, container, or dedicated host?
Use the weakest boundary that still satisfies your threat model, then verify every trust edge around it. The following comparison reflects the qualitative trade-offs documented by OpenAI, Docker, Microsoft, Redis, vLLM, and Kubernetes.
| Option | Kernel and escape boundary | Filesystem and credential exposure | Network and service controls | Timing and noisy-neighbor exposure | Operations, cost, and fit |
|---|---|---|---|---|---|
| Hardened container or process sandbox | Usually shares the host kernel; an escape affects the host and peer workloads | Depends on mounts, user IDs, runtime hardening, and secret handling | Requires explicit egress policy and careful control of host services | Shares host resources and hardware side channels | Lowest operational overhead; suitable for same-trust, low-sensitivity agents on a trusted host |
| MicroVM or equivalent strong sandbox | Each sandbox has its own Linux kernel; the hypervisor adds a boundary around the guest | Guest filesystem is separate unless paths are mounted; credentials can stay outside the guest | Hypervisor, network, runtime, workspace, and credential controls can be layered | Still shares physical hardware unless placed on dedicated hosts | More overhead than a container, but a practical default for untrusted prompts or code execution |
| Dedicated host or node | Removes other tenants from the physical machine | Allows an independent storage and credential-management perimeter | Can use a separate network and service policy | Strongest protection from cross-tenant contention and processor-cache channels among these choices | Highest cost and capacity-management burden; appropriate for adversarial or regulated tenants |
| Shared cache with tenant keys and ACLs | Does not create a process or kernel boundary | Logical key permissions can limit reads and writes, but the cache process and memory remain shared | Identity-aware clients and command restrictions are required | Latency, eviction, memory, and timing remain shared | Lower cost and simpler lifecycle; acceptable only when logical isolation and shared-resource risks are acceptable |
| Per-tenant cache or inference instance | Separates the service process and its memory from other tenants | Independent data, deletion, backup, and access policies are possible | Can have a distinct endpoint, network boundary, and credentials | Reduces noisy-neighbor and timing exposure, though the underlying host may still be shared | Higher cost and operational work; use for strict isolation, predictable latency, compliance, or independent lifecycle |
Docker’s sandbox documentation describes a lightweight microVM with its own Linux kernel for each sandbox and treats hypervisor, network, Docker Engine, workspace, and credential isolation as separate controls. That layered model is more useful than assuming that “containerized” automatically means “isolated.”
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Create the boundary before the agent receives a prompt or tool permission. A reliable implementation sequence is:
- Allocate one sandbox per trust domain. Give each untrusted agent its own microVM or hardened sandbox. Do not place agents with different owners in one shared writable environment merely because they use the same model.
- Apply a deny-by-default network policy. Start with no outbound access. Add only the package registries, model endpoints, APIs, or internal services that the agent actually needs, and restrict destination, port, protocol, and duration where your platform supports it.
- Expose the smallest workspace. Mount only required paths. Prefer read-only mounts for reference material and clone- or snapshot-based workspaces when the agent does not need a live write-through view. A direct workspace mount is a live read-write trust edge: changes made by the guest affect the host-visible path.
- Keep credentials outside the guest. Store API keys and service credentials in a host-side broker or secret service. Inject short-lived, narrowly scoped authorization through a proxy or broker. Never bake raw secrets into an image, environment snapshot, source checkout, or mounted workspace.
- Do not expose the host runtime socket. A host Docker socket or equivalent control socket is effectively a host-management interface. If an agent needs to build or run containers, use an engine inside its sandbox or a separately isolated build service.
- Review local services and MCP endpoints. A forwarded port, local daemon, or MCP server may execute outside the guest boundary. Permit each service explicitly, authenticate it, and document what data and actions it can reach.
- Separate identities. Give every agent, tenant, and broker session a distinct identity so access logs can attribute reads, writes, tool calls, and failures. Revoke the identity when the run ends or when policy changes.
- Set resource ceilings. Limit CPU, memory, disk, process count, and execution time to reduce denial-of-service and noisy-neighbor impact. A quota does not replace isolation, but it prevents one agent from consuming the shared service needed by others.
Record the intended trust edges as configuration, not tribal knowledge. For each mount, port, egress rule, credential, and host service, state who owns it, why it is required, what data can cross it, and how it is revoked.
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How can multiple agents share a Redis-style application cache safely?
Use authenticated tenant context plus authorization checks on every cache operation. A key prefix organizes data; it does not, by itself, prove that the caller is allowed to use that prefix.
Derive the key from server-side identity
Use a format that makes tenant, agent, purpose, and object identity unambiguous:
tenant:{tenant_id}:{agent_id}:{purpose}:{object_id}The tenant identifier must come from an authenticated session, token, or server-side mapping. Do not accept a caller-supplied tenant_id or prefix as the only authorization check. Redis notes that cache leaks are frequently caused by missing tenant context on read and write paths, not by accidental key collisions.
Apply ACLs and restrict commands
Create a cache identity whose key pattern matches only that tenant’s namespace, such as ~tenantA:*, and grant only the command categories required by the application. Keep administrative identities separate from application identities. Test both allowed and denied operations, including reads, writes, deletes, expiration changes, list and stream operations, pub/sub, and script execution.
Remove whole-keyspace commands from tenant identities. Redis documents that key-pattern ACLs do not constrain commands such as FLUSHALL, FLUSHDB, and SWAPDB. A tenant that can invoke one of these commands can affect data outside its prefix, regardless of how carefully keys are named.
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Carry tenant context through every code path
Validate the authenticated tenant on every read, write, delete, queue, pub/sub, and background-job path. Background workers should receive a server-assigned tenant context and reject jobs that lack it or whose resource identifier belongs to another tenant. Do not rely on the web request handler to enforce a check that a worker, retry loop, or scheduled task can bypass.
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Alert on prefix mismatches, denied-key attempts, unexpected key access, cache misses that expose another tenant’s state, and unusual eviction or latency spikes. Include tenant and agent identities in audit records while redacting prompts, secrets, and payloads. Run automated tests that attempt cross-tenant reads, deletes, scans, pub/sub subscriptions, and administrative commands before each release.
These controls provide logical isolation. They do not create separate processes, memory pools, or I/O devices. A shared cache can still leak information through latency, eviction behavior, memory pressure, or operational mistakes, and one tenant can become a noisy neighbor.
When should I move from a shared cache to dedicated instances?
Choose a separate cache endpoint or instance when the requirement is about the resource itself, not merely the names stored in it. Microsoft’s Azure Managed Redis guidance describes per-tenant instances as higher-isolation and higher-cost than shared instances.
- Process or memory separation: Tenants must not share a cache process, heap, or memory resource.
- Predictable latency: A tenant cannot tolerate another tenant’s bursts, eviction storms, or persistence work.
- Independent lifecycle: A tenant needs separate migration, backup, deletion, upgrade, or restore timing.
- Compliance or network boundaries: Policy requires a distinct endpoint, network segment, service identity, or operational owner.
- Sensitive access patterns: Timing or eviction behavior could reveal information even when key authorization is correct.
- Resource strain: The shared instance is approaching memory, connection, CPU, or throughput limits and sharding or separate instances are justified by the operational cost.
Dedicated instances do not automatically mean dedicated physical machines. If the threat model includes processor-cache timing or a hostile co-tenant, place the service on dedicated nodes or hosts as well, or use a provider configuration that supplies that guarantee.
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Are model and prefix caches safe to share between agents?
No. A model-serving process can share internal caches even when application keys are isolated. vLLM states that tenants sharing one server process do not receive isolation from one another and that cache salting is not an isolation boundary.
Use a dedicated inference instance per tenant when model-cache contents, prompt prefixes, or access patterns are sensitive. If you must share a server, put a gateway in front of it that authenticates the tenant and scopes cache identifiers on the server side. Treat a salt as a mitigation for a specific prefix-cache timing or collision risk, not as a substitute for a separate process, memory space, or service identity.
Also account for hardware side channels. OpenAI’s red-team experiment with separate VMs exchanging synthetic secrets through processor-cache timing demonstrates that “no network” and “no shared writable file” are insufficient claims against mutually untrusted agents on shared hardware. Dedicated hosts, stronger scheduling controls, or accepting a residual side-channel risk are architectural decisions that should be recorded explicitly.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I isolate agents in Kubernetes?
Kubernetes describes multi-tenancy as a spectrum: “soft” isolation may be adequate for cooperative workloads, while “hard” isolation addresses stronger data-plane and control-plane threats. No single feature defines hard multi-tenancy.
- Use a namespace and service account per tenant or workload. Bind RBAC permissions only to the resources that identity needs in that namespace.
- Set default-deny network policies. Add explicit rules for model endpoints, brokers, caches, and required internal services. Confirm that egress and ingress are both covered.
- Apply resource quotas and limits. Cap CPU, memory, object counts, and other consumable resources so one tenant cannot exhaust a shared node or control-plane component.
- Provision storage dynamically. Give workloads distinct volumes and access modes. Avoid mounting a shared read-write volume unless cross-tenant collaboration is intentional and authorized.
- Use sandboxed containers or node isolation when needed. Namespace and RBAC controls do not stop a kernel escape or eliminate processor and resource side channels. Add a sandboxed runtime, dedicated nodes, or dedicated clusters according to the threat model.
- Escalate the control plane when namespaces are insufficient. Tenants that require cluster-wide administrative control, independent upgrades, or stronger blast-radius limits may need virtual control planes or separate clusters.
Review both planes separately. Data-plane isolation protects files, network flows, and workloads; control-plane isolation protects the Kubernetes API, identities, scheduling, and cluster-wide resources. A design can be strong in one plane and weak in the other.
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How should I test and operate the isolation?
Do not treat a configuration as proven until you exercise the paths an agent could abuse.
Pre-production checks
- Attempt to read, write, delete, scan, subscribe to, and flush another tenant’s cache keys using each application identity.
- Attempt to reach an unapproved IP, DNS name, port, metadata endpoint, host service, runtime socket, and forwarded MCP service.
- Verify that a read-only mount cannot be modified and that a clone or snapshot cannot write back to the source.
- Run the agent with an expired, revoked, and deliberately over-scoped credential; confirm that the broker denies each case.
- Fill a tenant’s quota and measure whether the failure is contained rather than taking down peer workloads.
- Inspect logs for tenant identity, agent identity, destination, cache key, and decision outcome without recording secrets or sensitive prompt contents.
Runtime response
If a cross-tenant cache read or unexpected host access is detected, stop the affected agent, revoke its broker token, preserve access and network logs, and identify every key, file, endpoint, and credential it could have reached. Rotate exposed credentials, invalidate affected tenant data, and redeploy from a clean image or snapshot. Keep the incident boundary-specific: a denied key attempt is different from a successful read, and a shared-host timing concern may require node or host replacement rather than a cache flush.
Change control
Review mounts, egress rules, service forwards, ACL patterns, command categories, model-cache settings, and Kubernetes RBAC whenever an agent gains a new tool. Treat a new tool as a new trust edge, even if the tool runs on localhost. Keep a written decision for why a shared cache, node, or host remains acceptable and what event triggers migration to a dedicated resource.
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Can I safely give two agents the same read-only source directory?
A read-only mount prevents those agents from modifying the directory, but it still reveals every file in that mount to both guests. Use separate, minimal snapshots when one agent should not learn another agent’s source, and keep secrets and generated outputs outside the shared path.
Is a Kubernetes namespace enough for untrusted code execution?
A namespace mainly organizes API objects and permissions; it does not provide a separate kernel or eliminate node-level side channels. For hostile code, add a sandboxed runtime and node controls, or use separate clusters when tenants need stronger control-plane and blast-radius separation.
What should I do if a tenant prefix appears in another tenant’s cache result?
Treat it as a security incident, not a cache miss. Stop the affected workers, revoke their broker credentials, preserve audit data, determine whether reads, writes, queues, or subscriptions crossed the boundary, invalidate exposed entries, and rotate any secret that could have been returned.
Does putting a random salt in an LLM cache key create tenant isolation?
No. Salting can reduce a particular prefix-cache collision or timing signal, but requests in the same server process still share cache structures and resources. Use per-tenant inference instances or an authenticated gateway that assigns and enforces tenant-scoped identifiers when stronger separation is required.
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