Your agents can be talked into anything. We find out first.
Uvy jailbreaks your agents, chains prompt injection through their tools and memory, and pressure-tests the guardrails, then hands your team the exact defenses that hold. Security that keeps pace with what you ship.
Every agent you ship is a new employee a stranger can talk into anything
We attack it, then we make it hold.
Every proven attack becomes a hardening task and a watch. Red team finds the way in; blue team closes it and makes sure it stays closed.
Uvy attacks your agents the way a determined adversary would: through the prompt, through the content they retrieve, and through the tools they can call.
- Jailbreaks and guardrail bypass
Drive the model past its safety, policy, and refusal boundaries to do the thing it was built never to do.
- Direct and indirect prompt injection
Through user input, and through the documents, emails, tickets, and web pages the agent reads, where a hidden instruction becomes a trusted command.
- Tool and function-call abuse
The confused-deputy problem: make the agent invoke its own tools with an attacker's intent, against systems the attacker could never reach directly.
- Excessive agency
Push the agent to take actions it should never be able to take, then escalate that into data access or a foothold in your systems.
- Memory and knowledge poisoning
Plant instructions in memory or the retrieval store that the agent trusts and acts on later, long after the attacker is gone.
- Data and system-prompt exfiltration
Extract secrets, the system prompt, tool schemas, and other users' data out through the model's own responses.
Every successful attack becomes a defense we help you build and a test that runs forever, so your guardrails keep pace with every model and prompt change.
- Guardrails that hold under attack
Input and output validation designed and tested against real injection, not a keyword blocklist that the first clever prompt walks past.
- In-context hardening
System-prompt and context defenses, instruction hierarchy, and the in-context learning patterns that measurably reduce injection success.
- Agent and permission inventory
Find the shadow agents across your org and map what each one can actually reach, so the blast radius is a decision, not an accident.
- Runtime behavior monitoring
Anomaly detection on agent actions and tool calls, so an agent acting out of character is caught while it is happening.
- Regression suites
Every jailbreak we find becomes a test in a suite that runs on every model swap, prompt edit, and tool addition. Security does not silently regress.
- Least-privilege scoping
Cut a compromised agent's reach: scope tools, gate high-impact actions behind approval, and separate identities so one breach is not total.
The full agent attack surface
Aligned to the OWASP Top 10 for LLM applications and the MITRE ATLAS adversary techniques for AI systems.
Aligned to the emerging standards for AI security
So an attestation from Uvy stands up to your buyers, your board, and the regulators writing the rules now.
Ship agents you can defend
An attack report on your agents
The exact jailbreaks and injections that worked against your agents, with reproductions, not a generic list of LLM risks.
Defenses that actually hold
The specific guardrails, prompt changes, and scoping that closed each attack, verified against the same adversary that broke it.
A living regression suite
Your attacks, kept and re-run, so a model upgrade or prompt tweak can never quietly reopen a hole.
An agent inventory
Every agent, its tools, and its real permissions, mapped, so you know your surface before an attacker does.
Continuous re-testing
Agents change weekly. Uvy re-attacks on your cadence, not once at launch.
Human in the loop
You approve scope and escalations. Uvy does the exhaustive adversarial work; the calls that matter stay with you.
What does it mean to red-team an AI agent?
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Uvy attacks your agents the way an adversary would: it jailbreaks them, chains direct and indirect prompt injection through their tools and memory, tests for excessive agency and tool abuse, and pressure-tests the guardrails, then proves what actually worked with reproductions.
Can you test agents built on OpenAI, Anthropic, or open models?
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Yes. Uvy tests the agent and its scaffolding, its prompts, tools, memory, and permissions, independent of the underlying model, so it works across commercial and open models and keeps working as you swap them.
What do we get back?
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The exact jailbreaks and injections that worked, the specific guardrails and scoping that close them, a living regression suite of your attacks re-run on every change, and an inventory of every agent, its tools, and its real permissions, mapped to the OWASP LLM Top 10 and MITRE ATLAS.
Bring Uvy to this surface.
Tell us about your environment and we will bring Uvy's offense and defense to it. One conversation to get started.
Or write to [email protected]