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HuggingFace

Secure runtime environments for AI agents.

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Problem

No matter how good detection gets, sooner or later something gets through. Most teams never prepare for what happens next.

Triquetra

You can't stop everything.

Every team building AI agents is trying to stop the next attack. Better prompt injection defenses, better jailbreak detection, and better guardrails. But you can't detect everything.

Borromean Rings

You don't know what's happening.

Nobody catches everything. New attacks show up. Zero-days happen. Something gets through. Now you're checking logs, revoking credentials, trying to find what the agent already touched.

Valknut

You find out too late.

Very few teams prepare for what comes after. Eventually, you figure out what the agent accessed, what it changed, and what it never reached. By then, you're just looking at the damage.

Solution

Every agent starts inside its own isolated environment. Think of it as giving every agent its own space to work in. Everything the agent can see, use, and interact with is defined before it starts. Whatever gets through stays inside that environment. It stops there. The outcome stays predictable.

Use Cases

USE CASE 01 // 06
USED FOR
Code generation PR reviews Repository maintenance Deployments
RISK

Coding agents often inherit the same access as the developer using them. One compromise can reach repositories, credentials, and production systems far beyond the task.

OUTCOME

Their work never extends beyond the scope they started with.

Frequentlyaskedquestions

Docker is built for packaging and deployment, not for containing a compromised process. An agent inside a container can still reach the network, read mounted files, and use whatever the container has access to. We enforce a layer below that, controlling what the agent can read, call, or reach, even inside a container.
Most AI security products focus on preventing attacks. We focus on what happens after one succeeds. You don't need another promise that prompt injection or jailbreaks will never happen—you need to know exactly what a compromised agent can still do. That's the problem we solve.
It depends, and that's the problem. Today there's no standard way to know how far a compromised agent can go. Every setup is different, and most teams don't know the real worst case until something goes wrong.
Not that attacks never happen, nobody can promise that. We promise that when one does, the outcome is already known. You can say, before anything goes wrong, exactly where the worst case ends. That's not true for most agent setups today.
It means knowing the answer in advance. Ask, "What's the worst this agent can do right now if it's compromised?" and you get a real answer, not a guess. That answer stays the same no matter which attack gets through.
No. Each agent runs in its own isolated space. A compromised agent can't reach another agent unless you explicitly allow it.
What the agent can read, what tools it can use, what network calls it can make, what commands it can run, and what systems it can access. If the agent can do it, we can enforce boundaries around it.
No. We only block things the agent was never supposed to do anyway. Everything it's meant to do keeps working exactly the same.
At the operating-system level, not by another program watching the agent. That matters because the agent can't see the boundary, so it can't find a way around it.
No. Since the rules run at the OS level, there's no extra service sitting in between adding delay.
You write a simple policy for what the agent can read, call, and reach, then turn it on with one command. It works with LangChain, CrewAI, AutoGen, LlamaIndex, OpenAI Swarm, or your own setup. Most teams are running in minutes.
No. Detection tools try to stop the attack. We handle what happens after one gets through. They work well together.

Everyone is competing to catch the next attack.
We're building for the one that gets through.

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