One aspect of multi-user agent security, which I think is underexplored, is how context from a higher-privileged session might "leak" into a lower privileged session. Suppose the CEO of a company uses your shared openclaw agent and the agent writes sensitive data (investor emails, for example) to its memory. My understanding is this is not protected by Alter. Then a contractor uses the same agent and can read that same memory. It seems to me that a better agent harness for this kind of multi-user use case is needed; the standard ones like openclaw and claude code simply are not designed for it. Would love your thoughts!
We actually account for this already! We allow developers to use alter to partition context memory based on end user + agent identity so the context leaks like the one you described can be fully prevented!
Thank you! Yes the goal is to allow developers to build multi-tenanted agents that can actually act on behalf of end users. We are in the process of abstracting away every building block needed to build that so developers can focus on what they do best
How do you handle permissions when the downstream service only offers a shared API key? For example, two sales reps can use the same CRM endpoint but should only access their own accounts. Does Alter check record ownership, or does the developer still need to supply that logic?
We allow end users to add API keys directly via the alter connect SDK. If the API key needs to be shared then developers can map it on their end using the grant ID abstraction that we provide. Feel free to go through the docs here: https://docs.alterauth.com/concepts/credentials/
This seems like a bad idea to me these days. Companies doing this make themselves a target and then get hacked like OpenCode. Customer accounts get drained or bills run up.
Run your own gateway behind a firewall / vpn. I personally use GoModel + Tailscale.
Following the same thread as souvik1997 here: the leak problem gets worse in shared rooms than in shared sessions. When several principals (humans plus their own agents) sit in one channel, the transcript becomes the privilege boundary. Whatever an agent read in a higher-privileged turn is sitting in the context of every later turn in that room.
What we hit building exactly that, rooms where humans and bots are peers: you can't scope a token to an agent, you have to scope it to an (agent, room) pair, and then decide what happens to the derived artifacts. Tool output, file handles, and the memory blob the agent writes back all outlive the session and get replayed into the next one.
So how are you handling derived state? Is agent memory a new principal holding the intersection of the rights of the sessions that fed it, or does it just inherit and you rely on the model not to act on stale context?
When several principals are in the same channel talking to the same agent the concept of agent delegation does not apply.
The right way to treat the agent is not as a delegated agent but rather as a stand alone agent with its own permissions and privleges.
In scenarios where an agent and humans are peers we recommend giving separate agent identities to the agent and treat them as separate privileged users and give them permissions based on their identity and not based on the identities of the users in the channel.
One question from the infrastructure side (i'm running a similar scenario but for infrastucture) where any mistake could not be reversible such as a deletion of a resource.
How do you handle an agent that has valid authorization but has been prompt-injected?
Hey @luis_valerio, kevan here! That question shaped most of the design.
We assume you can't reliably stop the model from being prompt injected. So nothing is enforced at the model layer. Enforcement happens at the proxy, which sits between the agent and the downstream API and is the only thing holding the credential. The agent never sees the key, so an injected agent can't bypass it or route around the deterministic policy.
For irreversible actions you stack three things:
1. Method/endpoint restrictions. "This agent may only GET under /instances/*" means a DELETE never reaches the provider. For infra agents we'd push read-only by default and allowlist the few mutations you need.
2. Human-in-the-loop on the dangerous ops. Approval can be required conditionally: on DELETE, on specific operations, or when a parameter matches (resource outside the allowed set, etc). The request is frozen server-side, so what the approver sees is exactly what executes. The agent can't swap the payload after the yes.
3. Audit logs on every call, including denials, with the agent and end-user identity attached. So you can see the agent tried a DELETE it wasn't allowed, on whose behalf, and with what body.
Basically we treat every call like a compromised laptop: don't assume it behaves, scope the credential, and gate the risky actions behind a second party. Would love to hear more about your setup.
souvik1997 | 14 hours ago
One aspect of multi-user agent security, which I think is underexplored, is how context from a higher-privileged session might "leak" into a lower privileged session. Suppose the CEO of a company uses your shared openclaw agent and the agent writes sensitive data (investor emails, for example) to its memory. My understanding is this is not protected by Alter. Then a contractor uses the same agent and can read that same memory. It seems to me that a better agent harness for this kind of multi-user use case is needed; the standard ones like openclaw and claude code simply are not designed for it. Would love your thoughts!
[OP] simfinii | 14 hours ago
Feel free to go through the docs here if you have more questions: https://docs.alterauth.com/guides/propagate-identity-to-memo...
souvik1997 | 13 hours ago
[OP] simfinii | 3 hours ago
Melatonic | 4 hours ago
[OP] simfinii | 4 hours ago
ptkachykby | 5 hours ago
I am the founder of Substrate, a project that lets you inspect what is in the memory and select what each agent sees.
Do you separate memory per user with your harness?
[OP] simfinii | 5 hours ago
avikonduru | 14 hours ago
[OP] simfinii | 14 hours ago
verdverm | 4 hours ago
This seems like a bad idea to me these days. Companies doing this make themselves a target and then get hacked like OpenCode. Customer accounts get drained or bills run up.
Run your own gateway behind a firewall / vpn. I personally use GoModel + Tailscale.
https://news.ycombinator.com/item?id=50133401 (OpenCode hacked story)
911viper | 14 hours ago
What we hit building exactly that, rooms where humans and bots are peers: you can't scope a token to an agent, you have to scope it to an (agent, room) pair, and then decide what happens to the derived artifacts. Tool output, file handles, and the memory blob the agent writes back all outlive the session and get replayed into the next one.
So how are you handling derived state? Is agent memory a new principal holding the intersection of the rights of the sessions that fed it, or does it just inherit and you rely on the model not to act on stale context?
[OP] simfinii | 14 hours ago
The right way to treat the agent is not as a delegated agent but rather as a stand alone agent with its own permissions and privleges.
In scenarios where an agent and humans are peers we recommend giving separate agent identities to the agent and treat them as separate privileged users and give them permissions based on their identity and not based on the identities of the users in the channel.
We support standlone agent identities that dont require end users as the principals. Can read about that here: https://docs.alterauth.com/guides/give-an-agent-scoped-acces...
911viper | 11 hours ago
mauvehed | 4 hours ago
[OP] simfinii | 4 hours ago
We have also gotten pen tested 2 separate times and have found no issues. You can find the result of one of the pen tests on the trust center
sirianni | 48 minutes ago
luis_valerio | an hour ago
How do you handle an agent that has valid authorization but has been prompt-injected?
kdodhia | 50 minutes ago
We assume you can't reliably stop the model from being prompt injected. So nothing is enforced at the model layer. Enforcement happens at the proxy, which sits between the agent and the downstream API and is the only thing holding the credential. The agent never sees the key, so an injected agent can't bypass it or route around the deterministic policy.
For irreversible actions you stack three things:
1. Method/endpoint restrictions. "This agent may only GET under /instances/*" means a DELETE never reaches the provider. For infra agents we'd push read-only by default and allowlist the few mutations you need.
2. Human-in-the-loop on the dangerous ops. Approval can be required conditionally: on DELETE, on specific operations, or when a parameter matches (resource outside the allowed set, etc). The request is frozen server-side, so what the approver sees is exactly what executes. The agent can't swap the payload after the yes.
3. Audit logs on every call, including denials, with the agent and end-user identity attached. So you can see the agent tried a DELETE it wasn't allowed, on whose behalf, and with what body.
Basically we treat every call like a compromised laptop: don't assume it behaves, scope the credential, and gate the risky actions behind a second party. Would love to hear more about your setup.