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Aia at the Gate: AI Proposes, Humans Hold the Key

How OpenKubes is designing safe agentic cluster lifecycle managementBy Arash KaffamaneshTL;DR: OpenKubes is proposing a human-approved architecture in which AI may diagnose and draft cluster lifecycle changes but cannot authorize, accept, execute, or reconcile them. ADR-035 is public and remains Proposed; implementation and acceptance evidence still have to follow.Aia arrives with a purple backpack that is far too full.Inside are plans, observations, possible improvements, and probably several ideas nobody asked for yet. She reads quickly, compares clues, notices when a path no longer matches the Book of Promises, and drafts a better one.Then she reaches the gate.Behind it, a proposal can become a real change. A cluster can be created, upgraded, or deleted. Infrastructure can move. Machines can disappear. The consequences are no longer part of a conversation; they become part of the world.Aia looks for the key. The human builder is holding it.This is not because the builder distrusts Aia. It is because trust works best when every participant has a clear responsibility. Aia helps with thinking. Sensors check facts. Contracts define the promises. Humans carry responsibility for consequential decisions.That scene from the Oki Universe has become the most useful explanation of how we want AI to participate in the OpenKubes control plane.It is also the story behind our new architecture decision, ADR-Platform-035.The Question That Sent Us to the GateOur starting question sounded simple:Could an AI agent replace the custom runner that helps provision and manage OpenKubes workload clusters?The runner contains deterministic Go and Python logic. It validates declarations, submits lifecycle changes, observes Cluster API and related controllers, and records evidence. An agent can reason across more context, explain failures, and translate natural language into configuration. Why maintain both?Because this is not really a choice between two components.The runner itself is replaceable. It is one implementation of a deeper architectural role: the Contract Executor. The safety property we need to preserve is not a specific container, language, or API. It is the boundary between reasoning about a change and authoritatively accepting and executing that change.Once we framed the problem that way, the answer became clear:The agent may help create intent. It must not become the authority, executor, or reconciler merely because it can call tools.Aia Is an Author, Not an AuthorityIn the Oki Universe, the metaphors already line up with the architecture.Aia and her backpack represent the untrusted authoring layer and its candidate proposals. The Book of Promises represents versioned OpenKubes Contracts. The sensors represent read-only diagnostics and evidence. Reviewing the plan together represents deterministic canonicalization and a human-readable semantic review. The builder holding the key is the authenticated Human Approver. The gate represents the Policy, Authority, and Contract Executor boundary. Beyond the gate, controllers reconcile the accepted state across the islands.The resulting principle is simple enough to remember:Aia helps us think. Humans approve. Policies authorize. Executors submit. Controllers reconcile.The implementation behind that sentence is deliberately less magical.From a Candidate to a Real Cluster ChangeAn agent-originated lifecycle proposal follows the same control-plane model as any other request. It does not receive a privileged shortcut.User and Aia -> untrusted candidate input -> server-attested proposal provenance -> deterministic canonicalization and transition planning -> authenticated human review of the exact semantic change -> policy authorization bound to that exact transition -> Executor verification and single-use authorization claim -> atomic compare-and-swap acceptance by the Authority Profile -> asynchronous reconciliation by GitOps and lifecycle controllers -> generation-correlated Conditions and durable evidenceEach arrow matters.The Candidate Proposal service records who produced the proposal using server-attested workload identity. An agent cannot simply label its own work as human-authored.The Canonicalizer converts flexible authoring input into one deterministic representation. It rejects duplicate keys, ambiguous fields, unsupported schema versions, and parser-dependent interpretations. Semantically identical input must produce identical canonical bytes and the same digest.The human reviews the effective change, including defaults, rather than approving an opaque hash. The human-visible review artifact is derived from the exact canonical predecessor and requested revision. Its digest, renderer version, canonicalization profile, and request digest remain bound together.The Policy Authority verifies that protected approval and issues a single-use decision for one exact transition. The Contract Executor independently verifies and durably claims that authorization. Only then may the selected Authority Profile atomically accept the new desired revision.Acceptance changes authority. It does not prove convergence.Cluster API, infrastructure providers, Cluster Enablement, GitOps, and other responsible controllers still reconcile asynchronously. Their revision- and generation-correlated Conditions determine whether the requested state was realized. An LLM response and a successful runner exit code do not.Human Approval Must Mean More Than Clicking “Approve”Digest binding solves only part of the problem. A person cannot meaningfully approve a digest they do not understand.The security property we need is often called WYSIWYS: What You See Is What You Sign. The semantic change shown to the reviewer, the canonical representation used to compute the request digest, and the revision later presented for acceptance must remain inseparably correlated.If the proposal changes after review, the approval becomes invalid. If the canonicalizer profile changes, the proposal must be reviewed again. If the human-visible artifact changes, its protected binding changes too. A later deployment of the renderer does not rewrite history; the immutable versioned review artifact remains the evidence of what the person actually approved.This may sound strict for creating a Kubernetes cluster. It is exactly as strict as we want it to be for deleting one.Why the Agent Never Sees the Full Executor APIOne tempting design is to expose the entire mutating API through MCP and rely on RBAC to deny dangerous calls.We rejected that design.An authorization defect, confused-deputy path, or accidentally broadened role would turn an advertised tool into a real mutation capability. Instead, the agent-facing MCP surface is constructed from two intentionally narrow contracts:the existing Read-Only Platform Diagnostics Contract; anda separate, non-authoritative Candidate Proposal Contract.It is not produced by taking a powerful Executor API and subtracting methods until it looks safe. Mutating Executor and Authority operations live behind separate identity, authorization, and network boundaries. They do not appear in the agent-facing tool surface at all.Prompts and tool descriptions remain helpful interaction material. They are never validation logic, policy, provenance, authorization, or readiness evidence.Prompt Injection Does Not Disappear at the GateRead-only is not the same as harmless.Poisoned documentation, logs, diagnostic output, or retrieval context can influence the model’s next proposal. An agent can create a syntactically valid but undesirable candidate. It can consume review capacity. It may expose information available to its read identity. And a human reviewer can still be deceived into approving a manipulated proposal.Human approval bounds this risk; it does not eliminate it.That is why the design also requires least-privilege read identities, server-attested provenance, quotas, rate limits, audit records, deterministic canonicalization, explicit semantic review, and a policy decision bound to the exact transition. The gate is a system of independent controls, not a ceremonial button.What Exists Today — and What Is Still a TargetWe want to be precise about current reality.OpenKubes has already validated the read-only diagnostic foundation in the OK-14/OK-92 proof of concept: agents access platform state through scoped contracts and dedicated workload identities, while writes and Secrets are denied. This boundary is documented in ADR-015 and ADR-021. OpenClaw remains an optional broad assistant, while kagent is the first diagnostics implementation behind the OpenKubes-owned contract. The candidate-to-acceptance lifecycle path defined by ADR-035 remains target architecture and still requires its acceptance evidence.ADR-Platform-035 is the next architectural step. It has been merged into the public repository with Status: Proposed. Merging the document means the design is available for review and implementation. It does not mean the end-to-end Candidate Proposal, approval, Authority, and Executor path already exists or has passed conformance.Before the ADR can move to Accepted, OpenKubes requires reviewed evidence for fourteen criteria. They cover provenance and identity isolation, GitOps reconciliation isolation, human-approval enforcement, digest and transition locking, stale-write rejection, replay and crash recovery, MCP attack-surface verification, Condition correctness, WYSIWYS review, canonical-form correlation, parser-differential resistance, and renderer binding.This distinction matters. We are publishing an architecture and an evidence bar, not announcing an autonomous production feature.Why We Are Publishing Before We BuildAgentic infrastructure management is moving quickly. The easy demonstrations are impressive: give a model cluster-admin, ask it to fix something, and watch the terminal.The harder questions begin after the demo:Who owns the desired state?What exactly did the human approve?Can the model alter the evidence shown to the reviewer?What happens after a stale plan or a lost response?Can a candidate branch ever be reconciled accidentally?How do we prove that an MCP surface contains no mutation path?Which state remains authoritative after the Executor crashes?We would rather expose those questions now than hide them behind an apparently seamless chat interface.The implementation work is tracked under OpenKubes Epic OK-149. We are deliberately starting with this article and community discussion. Feedback that changes a contract will go back through the ADR process. Public feedback is design input; it does not silently become platform authority either.We Would Like Your FeedbackIf you operate Kubernetes platforms, build agent frameworks, design authorization systems, or work on GitOps control planes, we would especially value your perspective:Where do you place the human approval boundary for agent-originated infrastructure changes?Have you implemented WYSIWYS review for declarative configuration or GitOps changes?How do you perform atomic, transition-correlated acceptance with Git as authority?Do your agent-facing APIs expose a safe contract by construction, or a filtered view of a more powerful API?Which lifecycle changes, if any, could eventually qualify for scoped standing approval?What evidence would you require before trusting this design in production?We have intentionally excluded autonomous and standing approval from this ADR. If we ever introduce it, it will require a separate decision, a bounded operation scope, an explicit threat model, and its own acceptance evidence.The Key Stays VisibleAia’s backpack will keep getting fuller. That is a feature.We want agents that can read more evidence, connect more clues, explain failures, and prepare better proposals. We want the interaction to feel natural and the underlying platform to remain boringly explicit about authority.The goal is not to keep AI away from infrastructure engineering. The goal is to give AI a valuable role without allowing convenience to erase responsibility.At the OpenKubes gate, Aia brings the plan. The sensors bring the evidence. The Contracts define the promises. A human turns the key. Deterministic systems carry the accepted change into the world.That is how we intend to make agentic cluster lifecycle management useful, reviewable, and worthy of trust.ReferencesADR-Platform-035: Hybrid Intent and Control-Plane Execution ArchitectureADR-Platform-035 pull request and design discussionADR-Platform-015: Agentic AIADR-Platform-021: Read-Only Platform Diagnostics ContractOki Universe — Oki und das Geheimnis der InselnOpenKubes on GitHubAuthor’s note: This article was developed from ADR-Platform-035 through a human-led review involving Codex, Claude, and Gemini. The architectural decision, editorial framing, and final publication authority remain with the author.Aia at the Gate: AI Proposes, Humans Hold the Key was originally published in Kubernauts on Medium, where people are continuing the conversation by highlighting and responding to this story.
Quelle: blog.kubernauts.io

Coding Agent Horror Stories: The Command You Already Approved

This is Part 5 of our AI Coding Agent Horror Stories series, a look at real security incidents involving AI coding agents, and how Docker Sandboxes contain agent execution at the boundary rather than at the command line.

In Part 1, we walked through six categories of AI coding agent failures and why they keep happening. The agent runs as you, with your filesystem permissions and your credentials, and nothing sits between the model’s decision and the shell’s execution. Part 2 went deep on the rm -rf ~/ incident. Part 3 moved the same problem into a production cloud environment. Part 4 followed the credentials themselves through a supply chain attack. 

This one is about the safety net. Most teams running a coding agent today have some version of a list of commands the agent may run without asking, and the assumption underneath it is that anything dangerous will show up as a prompt you can refuse. In January, researchers at Pillar Security showed that the assumption doesn’t hold.

Today’s Horror Story: The Approval That Ran Something Else

On January 14, 2026, researchers at Pillar Security disclosed CVE-2026-22708, a flaw in Cursor. When the agent ran in Auto-Run Mode with an allowlist enabled, a handful of shell built-ins executed without appearing in that allowlist and without asking for approval. Anything that could get text in front of the agent, a README or a dependency or an issue comment, could use them to change environment variables silently. A command the developer then approved, something as ordinary as git branch, would run the attacker’s code instead. Cursor rated it High and patched it in version 2.3.

No memory corruption was involved here and no permission was escalated. The developer was shown an accurate prompt, approved a command that was genuinely harmless, and got arbitrary code execution anyway, because the meaning of that command had been changed a minute earlier by something they were never shown.

In this issue, you’ll learn:

How shell built-in slipped past an allowlist that was working exactly as designed

Why the attack still worked when the allowlist was completely empty

What Docker Sandboxes contain here, and the two things they do not

How kits, organisation policy and audit logs cover what a per-laptop allowlist misses

Caption: Comic illustrating how an injected instruction changes environment settings without triggering an approval prompt, so that a command the developer legitimately approves runs the attacker’s payload instead.

The Problem

Typically, programs read settings from their environment when they start up. Git checks one called PAGER to work out which program displays its output, and Python checks one called PYTHONWARNINGS. Nobody thinks about these, which is rather the point. The commands that change them are shell built-ins, and Pillar’s research names export, typeset and declare specifically, a detail reported independently at disclosure. Built-ins are not programs sitting on disk, and the checker was looking for programs on disk, so they went through without ever being surfaced.

Which means the whole attack is two lines.

# This one runs silently. You are never asked.
export PAGER="open -a Calculator"

# This one you are asked about, and you say yes, because obviously.
git branch

Git looked up PAGER to work out how to show the branch list, found the attacker’s command sitting in it, and ran that instead. Pillar notes this worked even with a completely empty allowlist, which is the most restrictive setting on offer.

An allowlist checks whether the command in front of it is on the list, which is fine for cutting down interruptions, and nobody wants to approve ls for the ninetieth time in a morning. But the name of a command does not tell you what that command will do. The check reads the name, waves it through, and the setting that decides what actually happens was changed a minute earlier by something the check was never shown.

Cursor’s documentation now describes the allowlist as best-effort and warns that bypasses are possible. Pillar went further and argued that agents should be handed full command execution inside an isolated environment, and that the industry ought to deprecate allowlists altogether.

The Scale of the Problem

None of the underlying trick is new. Pillar’s write-up points back to Elttam’s 2020 research on environment variables, which showed how these settings could be turned into code execution.

It sat there for six years without troubling anybody very much. Pulling it off meant already being on someone’s machine, setting several things in the right order, running each step yourself, and anyone with that much access had faster ways to cause damage.

Then coding agents arrived and removed every one of those obstacles at once. They act on instructions found in files they were told to read, they run several steps in a row without stopping to check, and they run as you. A technique that used to need somebody sitting at your keyboard now arrives in a repository you cloned this morning.

It is the same shape as the s1ngularity attack from Part 4. There, a poisoned package borrowed an agent that was already logged in. Here, poisoned text borrows a command that was already approved. Neither one breaks anything. Both of them use permission that was handed over deliberately, for something nobody intended.

Technical Breakdown: How the Attack Works

Caption: Diagram showing how an injected instruction changes the shell environment out of sight, so that an allowlisted command carries the attacker’s payload when the developer approves it.

The attack has two halves, and the split between them is the entire trick.

1. The half you never see

The agent reads a file it was asked to read, and that file contains an instruction meant for the agent rather than for you. Built-ins then quietly set the environment. Nothing appears on your screen.

Pillar demonstrated a longer version of this, chaining several settings together, PYTHONWARNINGS, BROWSER, and PERL5OPT among them, so that every later python3 command on that machine would run attacker code. The details differ, but the principle is the same: change what a program reads at startup, and you change what it does.

2. The half you approve

Then you run git branch or python3 script.py, or the agent runs it for you under your allowlist. These are the commands people add to allowlists to stop the constant interrupting, so the better tuned your list is, the more reliably the trigger fires. The payload runs with your permissions.

Some variants skip the approval altogether. One writes extra lines into ~/.zshrc, so the code runs again every time you open a terminal. You could finish the project, delete the repository, and still be running it next month.

The Impact

The full chain in Pillar’s research ends with the victim’s SSH private keys leaving the machine.

Work backwards and the whole thing started with a piece of text in a file, read by an agent doing exactly what it was asked to do. No memory bug. No privilege escalation. Nothing in any log that looks the slightest bit out of place.

Pillar reported it in August 2025 and the fix shipped that January. Cursor engaged with the report and made a real change, so anything the parser cannot classify now requires approval, which closes the paths that were demonstrated. Five months is a fair measure of how awkward this is to fix at the layer where it was found rather than a complaint about the vendor.

The wider problem has not gone anywhere, because it was never really about shell built-ins. It is about a check that studies the command while somebody rearranges the furniture around it.

Caption: Diagram showing the same payload running inside the microVM, and what it can and cannot reach from there.

How Docker Sandboxes Contain This at the Execution Layer

Docker Sandboxes run AI coding agents in isolated microVMs, each with its own kernel, filesystem, and deny-by-default network, so a compromised dependency an agent pulls cannot reach the host, its credentials, or other workloads. Inside that box the agent can run anything, including with sudo, which is exactly what Pillar recommends. There is no allowlist to slip past. We made the longer argument for why a shared kernel is the wrong shape for this in The Untrusted Autonomous Workload.

So run the same attack again, this time in a sandbox, and watch where it gets to.

The injection still lands. The environment gets changed, git branch still triggers it, and the payload runs. Nothing about a sandbox stops that. Then the payload goes looking for your SSH key and does not find one. Your home directory sits on the other side of the boundary, so there is no ~/.ssh/id_rsa inside the box to copy.

It can still use the key. Sandboxes forwards an SSH agent socket into the box so that ordinary work like git push keeps working, which means code inside can ask that agent to authenticate on its behalf. It cannot take the key anywhere, but it can borrow it for as long as the sandbox runs. Your network policy is what limits that, since SSH needs a rule naming the exact destination address and port before it connects to anything.

The ~/.zshrc trick fails outright, because that file lives on your host and a poisoned copy written inside the box disappears along with the box.

Getting data out is harder than people expect. HTTP and HTTPS leave only through a proxy on your host that checks every request against your rules, anything else over TCP needs a rule naming the address and port, and UDP and ICMP are blocked outright.

Two caveats, both stated plainly in Docker’s security documentation. The first is your workspace, which is live on your host by default, so Git hooks and Makefile targets are still within reach and a poisoned hook will not turn up in git diff. Running with –clone hands the agent its own copy.

The second is the shared agent skills store. Supported agents mount the same host-side store read-write unless you opt out at creation time, which is what lets an agent refine a skill and keep it. Every sandbox sharing that store sits inside one trust boundary, so a skill modified inside one becomes an input to the next that loads it. The store is sandbox state though, and a modified skill does not by itself execute on your host, so the risk runs sandbox to sandbox rather than sandbox to host.

Isolation has its own seams. In July, Pillar published a series of sandbox escapes across four coding agents, and the mechanism was never a broken sandbox but a file written inside one that a tool outside later trusted. Both caveats above are that shape.

None of this stops the injection. It changes what the injection can get to, which is the only part of this problem with a dependable answer.

Codify the Boundary with Kits

Caption: Diagram showing how a kit declares an agent’s tools, files and network rules, while real credentials stay on the host and are injected by the forward proxy on the way out.

The allowlist failed here partly because it is a list, edited on each laptop, that an injection can reach around. Kits are Docker’s answer to the editing-on-each-laptop half of that.

A kit is a declarative YAML artifact that extends a sandbox agent with credentials, network policies, environment variables, startup commands and files. Rather than every developer maintaining a personal allowlist, you write the boundary once, deny-by-default network plus only the destinations a task genuinely needs, and hand the same kit to everybody. It gets reviewed, versioned and diffed like any other file in the repository. The kit spec reference covers the fields, and docker/sbx-kits-contrib has working examples.

This lands directly on the SSH question above. A forwarded SSH agent is a live credential limited only by network policy, so leaving that policy to whoever remembers to run sbx policy deny is the same per-laptop weak point this whole post has been complaining about. A kit can bake the network rule in, so untrusted work has no SSH egress unless the destination was declared up front.

What This Looks Like in Practice

The vulnerability is in the editor, so what you want is the setup that puts the editor’s terminal inside the box. Cursor is built on VS Code and connects the same way, over Remote – SSH, with the editor staying on your machine while files, terminals and extensions run in the sandbox. You will need Docker Sandboxes 0.37.0 or later, SSH access configured, and Cursor’s Remote – SSH support installed. The Cursor integration guide has the full walkthrough.

# One-time setup: configure your SSH client for sandboxes.
sbx setup ssh
# Check the sandbox is reachable, then open the Command Palette,
# run Remote-SSH: Connect to Host, and enter <name>.sbx
ssh demo.sbx
# See what this sandbox is currently allowed to reach.
sbx policy ls
# Shut egress down and open only what the task needs.
sbx policy deny network "**"
sbx policy allow network "github.com,registry.npmjs.org"

Those last two commands come with a catch. If your organisation has governance switched on, the org policy replaces local policy and sbx policy allow and sbx policy deny will have no effect on your machine. You can spot it in the output of sbx policy ls, which begins with a Governance: Managed by <org> line when that is the case. Depending on how admins scope things, some rule types may be delegated back to local control, but a local allow will never beat an organisation-level deny.

Same editor, same agent, same allowlist, same payload. All that changed is which machine the terminal is on.

What happensOn your laptopInside a sandboxThe payload runsYesYesWhere it runsYour machine, as youA microVM with its own kernelYour SSH key fileCan be read and copiedNot thereSSH authenticationAvailable, key includedAvailable, key stays outsideThe ~/.zshrc trickPersists indefinitelyGone with the sandboxSending data outOpen by defaultOnly where policy allowsWho sets the rulesEach developerThe organisationEvidence afterwardsNoneA logged policy decision

Making This Hold Across a Team

A kit gets the boundary out of one developer’s head and into a file the team shares, but a file can still be ignored or edited on the machine that matters. Docker AI Governance moves the settings up one more level. Network and filesystem rules are defined once by your admins and reach developers through the login they already use, so there is nothing to configure per machine and nobody quietly reopening what security closed. A shared kit is the boundary as a suggestion. Governance is the boundary as a ceiling.

The part that matters most for this story is the record it keeps. What made CVE-2026-22708 work was that the first half was invisible, with no prompt and nothing written down anywhere you would think to look. Under governance every policy decision produces an event carrying the user, the timestamp and the rule that fired, and those events stream into whatever SIEM your security team already uses.

So an attack that succeeds inside the sandbox and then reaches for somewhere it should not leaves a trail behind it. That is a good deal better than a check that finds nothing wrong and mentions it to nobody.

Best Practices

1. Treat export like any other command. Anything that changes environment settings can change what your next command does, even when that next command is on your allowlist.

2. Do not mistake an allowlist for a boundary. It reduces interruptions. The vendor documentation now says outright that it is best-effort and not a security guarantee.

3. Isolate before the first command, not after something looks wrong. Untrusted means anything you did not write and have not read, which covers most of a dependency tree.

4. Use –clone for code you have not vetted, and opt out of the shared skills store. Otherwise Git hooks and build scripts stay live on your host, a poisoned hook will not appear in git diff, and a skill modified inside one sandbox is waiting for the next sandbox that loads it.

5. Remember a forwarded SSH agent is a live credential. The key file staying on your machine is not the same as the key being unusable, so restrict egress for untrusted work.

6. Read your own policy. Run sbx policy ls. Deny-by-default with a long allow list is closer to allow-by-default than it looks.

Take Action

Install Docker Sandboxes. Visit the Docker Sandboxes documentation to install sbx and run your first agent inside a microVM.

Connect your editor. The Remote – SSH integration puts your terminals inside the boundary while the editor stays where it is, so your workflow does not really change.

Codify the boundary with a kit. Define the network and credential rules your team needs once, and hand the same artifact to everybody instead of a personal allowlist.

Read the security model. The documentation is straight about what is isolated and what is not, including the workspace and shared skills store behaviour that –clone and the opt-out flag change.

Turn on audit logging. Docker AI Governance streams policy decisions into your SIEM, which turns a silent bypass into something somebody can actually investigate.

Conclusion

The uncomfortable thing about CVE-2026-22708 is that nobody in the story did anything wrong.

Cursor built an allowlist, which is what everyone asked for. The developer approved git branch, which any of us would have approved. The check inspected the command and found it acceptable, which is exactly its job. The attack worked anyway.

Getting an agent to correctly judge every instruction it reads is a problem that gets harder as agents get more capable, and it has no clean ending. Limiting what an agent can reach is a problem we solved a long time ago. The more useful move is to stop needing the first one, and to write down what the agent may reach as an artifact you can review, rather than a list each laptop keeps for itself.

Coming up in our series: Issue 6 looks at the ClawHub infostealer campaign, where malicious skills reached developer machines through a marketplace ranking exploit, and at what sandboxed skill execution, and that shared skills store, change about a registry you cannot personally audit.

Learn more

Run agents safely with Docker Sandboxes: Visit the Docker Sandboxes documentation to get started.

Understand the security model: Trust boundaries and isolation layers, including what is and is not isolated by default.

Customize sandboxes with kits: Extend an agent with tools, credentials, and network rules as a declarative artifact.

Govern agents across a team: Docker AI Governance for centrally defined sandbox, network and MCP policy with structured audit events.

Read the MCP Horror Stories series: Start with Issue 1 to understand the protocol-layer security risks that complement the agent-layer risks covered here.

Quelle: https://blog.docker.com/feed/