vlno is the security layer for agentic AI. We produce adversarial training data, security evaluation environments, and reinforcement-learning trajectories that harden frontier models against indirect prompt injection and other attacks — continuous AI safety baked into the model weights, not bolted on at runtime.
Frequently asked questions
- What is vlno?
- vlno (from “Vulnerability — No.”) is a stealth AI security company based in Tel Aviv. It is the security layer for agentic AI: it produces the adversarial training data that hardens frontier models against the attacks they haven’t seen yet.
- What is indirect prompt injection?
- Indirect prompt injection (IPI) is an attack in which malicious instructions are hidden inside untrusted content an AI agent reads — a web page, email, PDF, document comment, or tool response — causing the agent to take harmful actions such as exfiltrating confidential data. Defending agentic systems against it is vlno’s focus.
- What does vlno build?
- One pipeline, three outputs: adversarial attack generation, sandboxed security evaluation environments, and reinforcement-learning trajectories that plug directly into a model’s fine-tuning pipeline.
- How is vlno different from guardrails or red-team reports?
- Runtime guardrails wrap a model and leave it vulnerable underneath; red-team reports only name weaknesses. vlno produces the training data that makes the model itself stronger — safety baked into the weights, not bolted on afterward.
- Where is vlno based, and is it hiring?
- vlno is based in Tel Aviv and is building a small, senior team across adversarial research, platform engineering, ML / post-training, and operations. Reach out at hello@vlno.ai.
Built to resist manipulation.
Everyone else tests your model or filters around it. vlno hardens the model itself, so the agents you build can't be turned against you.
Open models are here. The exposure is real.
An agent built on a model can be talked into working for someone else, using the access you gave it.
Most of the market
Filters around the model. Hands you a report. The model stays vulnerable.
vlno
Hardens the model itself, in the weights. So the agent built on it can’t be hijacked.
We don’t take away what the agent can do. We teach it when to do it.
Hardening a model isn’t about removing its abilities, that would break the very use cases you deployed it for. It’s about judgment. We teach the model to tell a real instruction from data that only looks like one, so a command buried in a document, an email, or a tool result no longer moves the agent to act.
Capability preserved
The agent keeps every action it needs. It can still delete, send, pay, and query. Nothing is locked away.
Instruction, not data
It learns to recognize a genuine instruction and ignore adversarial text hidden in the content it reads. The injection stops working.
Legitimate and in policy
It acts on instructions from an authorized operator, within the boundaries you set. The wrong action simply doesn’t happen.
This works alongside your runtime controls, not instead of them. Filters and guardrails stay in place on top. We change what the model does when an attack gets past them.
And it never stops.
vlno keeps attacking and re-hardening on its own, so your models and agents stay protected as both keep changing.
Attack
Our adaptive attacker tries to hijack the agent, the way a real adversary would.
Find the breakHarden
Every success becomes training that makes the model itself resistant, in the weights.
Fix the modelRe-harden
As new versions ship and new attacks emerge, it runs again. On its own.
Stay aheadThis isn’t theory. It holds where it’s hardest.
Frontier labs harden their own models this way, in the weights, before anyone trusts them. Our team did that work inside leading frontier labs. Now we bring it to the open models and agents enterprises actually run, and prove it where a wrong action has real consequences.
Built by the people who did it inside frontier labs
Not adapted from the outside. Our team hardened models inside frontier labs, and built vlno to do it for the open models enterprises run.
Proven in production
Already hardening agents that run in real enterprise environments, where the stakes are financial and operational.
Trusted by leading companies
Working with leading companies to bring robustness to governed, sovereign agentic deployments.
Already working with teams at the frontier and enterprises putting real agents into production.
Putting open models into production?
Tell us what your agents do, and we will make them hard to hijack, and keep them that way.
or email hello@vlno.ai