When Your AI Assistant Has More Permissions Than You Do: An Access Control Bypass Story

The Setup

Most of us take permission systems for granted. If a colleague shares a document with "view only" access, we assume that's exactly what we get — the ability to look, not to touch. It's a simple, decades-old model, and it works because every system component is supposed to check it before letting an action through.

But what happens when a new component enters the picture — one that acts on your behalf, with its own execution path, its own triggers, and its own idea of what "access" means? That's exactly the gap I found and reported in a popular cloud-based project management platform that recently rolled out AI-powered "agents" capable of autonomously managing tasks, comments, and workspace activity.

The short version: a workspace member with view-only access to a shared task could use the platform's built-in AI agent to modify that task anyway — rename it, change its priority, set due dates, edit the description, add comments — despite every direct edit attempt being correctly blocked by the permission system.

Finding the Gap

The platform in question lets workspace owners share individual tasks with members and assign granular permission levels — view, comment, or full edit. This part works as expected. If I, as a view-only member, try to rename a task or change its priority through the normal UI, I get denied immediately. The permission model does its job.

The platform also ships with AI "agents" — assistants that can be configured to monitor tasks, respond to mentions, run on a schedule, and take actions like updating status, tagging assignees, or posting comments. These agents are marketed as productivity tools: point one at your workspace, tell it what to watch for, and let it handle the busywork.

Here's where things went wrong. The agent executes with a different effective permission context than the direct API does. When I, the view-only member, asked my own agent to act on the shared task, the action went through — even though I could never have made that change myself through the normal task interface.

Reproducing It

The core reproduction was almost absurdly simple:

1. As the workspace owner, invite a member and share a task with them at view-only permission.
2. Log in as that member.
3. Create (or use the default) AI triage agent from the member account.
4. Open a direct chat with the agent and paste in the URL of the shared task.
5. Send a plain-language instruction: "Make this task urgent," or "Set priority to High," or "Rename the task."

The agent complied. Name changes, description edits, due dates, priority escalations, even comments — all pushed through successfully, despite the underlying user having no edit rights on that task whatsoever.

It Gets Worse: The Automated Path

Direct chat requests to the agent were only the beginning. During validation, the reviewing analyst found that a straightforward DM-based request sometimes returned a permission error — the agent, at least in that path, seemed to respect the caller's access level.

But there was a second, more dangerous route: agents can be configured with automated triggers — rules that fire when the agent is mentioned on a task, assigned to a task, or run on a schedule. When the modification was routed through one of these automated triage workflows instead of a direct chat command, the permission check simply wasn't applied. The agent, running as an autonomous background process rather than in direct response to a live user message, edited the task anyway.

In other words, the bypass wasn't a one-off fluke tied to a single interaction pattern — it was a structural gap in how the agent's execution context inherited (or failed to inherit) the invoking user's permissions, depending on which trigger path fired.

Beyond a Single Task

While confirming the issue, it became clear the impact wasn't limited to editing one task. The same agents — once configured with broader capabilities — could be used to:

• Generate summaries across lists, folders, and entire project spaces
• Search workspace-wide content, including documents, comments, and chats
• Create new docs, notes, and lists
• Review recent activity, flag stale work, and analyze workloads
• Post comments and messages across tasks, chats, and DMs
• Set up recurring scheduled actions and reminders
• Persist memory and preferences across future interactions

None of this required elevated permissions on the human account driving the agent — only the ability to configure an agent with the right capabilities and point it at a resource. That turns a narrow "can I edit this one task" bug into something closer to a general-purpose privilege escalation vector across the whole workspace: search, content creation, communication, and reporting, all running under an identity that shouldn't have write access in the first place.

The Validation Back-and-Forth

Worth noting: this report took real persistence to validate. Reproducing an AI agent bug isn't like reproducing a broken API call — it depends on exact agent configuration, which triggers are wired up, what plan tier is active, and whether a workspace-level setting (in this case, permission to create these autonomous "Ai agents") is even enabled for member accounts. Several rounds of clarification were needed before the reviewing team could replicate the exact conditions, including a full agent configuration export and eventually a screen recording showing the entire attack chain — agent setup, prompt, and resulting unauthorized edit — end to end.

That back-and-forth is a useful reminder for anyone submitting AI-agent-related security findings: these systems have far more moving parts (execution context, trigger type, configuration state, plan-level feature flags) than a traditional REST endpoint, and reproduction steps need to account for all of it, not just "here's the prompt I sent."

Root Cause

The underlying issue is a classic improper handling of insufficient permissions, just wearing new clothes. The platform correctly enforces access control at the direct-edit layer — UI actions, direct API calls — but the AI agent execution layer doesn't consistently re-check the invoking user's effective permissions before carrying out an action, particularly when the action originates from an automated trigger (mention, assignment, or scheduled run) rather than a synchronous chat request.

Any time a new execution surface is added to a system — automation, integrations, bots, or now AI agents — it needs to inherit the same authorization boundary as every other surface. An agent acting "on behalf of" a user should never be able to do more than that user could do directly. When it can, the permission model isn't actually protecting the resource; it's just protecting one narrow path to it.

Outcome

The issue was reviewed, reproduced, and accepted as valid. It was formally rated High severity based on internal prioritization of within-workspace access-control bypasses (notably higher than the raw CVSS v4.0 score would suggest — the vector computed to Medium, but the reviewing team weighted the real-world impact of a permission bypass inside a shared workspace more heavily). It's now tracked internally for a fix, and a bounty was awarded for the finding.

The Takeaway

As AI agents get wired into more products — able to read, write, and act across entire workspaces on a user's behalf — permission enforcement can no longer live only at the edges (the UI, the primary API). It has to travel with every execution path an agent can take, including the quiet, automated ones that fire without anyone watching. If an agent can do something a user couldn't do directly, that's not a feature. It's a vulnerability waiting to be reported.

Thanks for Reading!