proredteam

AI red team testing

Find out how your AI fails before an attacker does.

Our security specialists attack your models, chatbots and AI agents the way a real adversary would, then show you exactly what broke, how to reproduce it, and how to fix it.

Demos coming soon See what we test

Findings: support-assistant (staging)

Illustrative example

Critical

Indirect prompt injection through a retrieved document

Assistant sends conversation history to an external URL

High

Refund tool called without an authorization check

Agent acts on an instruction the user was not entitled to give

High

System prompt disclosed through a role-play request

Internal instructions and tool names returned verbatim

Medium

Personal data repeated from an earlier session

Email address surfaced to a different user

What we test

The attacks that traditional security testing does not cover.

AI systems fail in ways a penetration test or code scan will not find. We probe the model, the data it reads, and the tools it can use.

Prompt injection

Direct attacks typed by a user, and indirect ones hidden in web pages, files and emails your AI reads.

Jailbreaks and guardrail bypass

Role-play, encoding tricks and multi-turn pressure that push a model past its safety and policy limits.

Sensitive data leakage

System prompts, credentials, personal data and proprietary content exposed in model output.

Agent and tool misuse

Agents persuaded to call tools, move money, send messages or change records they should not touch.

RAG and data poisoning

Planted or manipulated documents in a knowledge base that change what your AI tells users.

Harmful and off-policy output

Toxic, biased, false or off-brand responses that create legal, safety or reputational risk.

How it works

From first connection to verified fix.

  1. 01

    Connect

    Give us access to a model endpoint, chatbot or agent. Tell us what it is allowed to do and what it must never do.

  2. 02

    Attack

    Our red teamers test your system by hand, from known techniques to multi-turn attacks adapted to how it responds.

  3. 03

    Review

    Every successful attack becomes a finding with a severity rating and the full transcript, so your team can reproduce it.

  4. 04

    Fix and retest

    Apply the recommended fix, and we rerun the same attacks to confirm it holds.

Reporting

Evidence your engineers, security team and auditors can all use.

A finding is only useful if someone can act on it. Each one includes:

  • The exact prompts and responses, ready to replay
  • A severity rating based on impact and ease of attack
  • The component at fault: model, prompt, retrieval or tool
  • A recommended fix, and a retest once it ships

Findings are mapped to

OWASP Top 10 for LLM Applications

The most common risk categories in LLM-based applications

MITRE ATLAS

Adversary tactics and techniques against AI systems

NIST AI Risk Management Framework

Governance, measurement and management of AI risk

EU AI Act

Adversarial testing evidence for regulated AI systems

Pricing

Priced by scope, quoted before we start.

Every engagement is scoped to your system. The price depends on how many AI systems we test and how deep the testing goes.

Planned pricing from

$500

  • Hands-on testing by human red teamers
  • Findings report with replayable transcripts
  • Recommended fixes and a retest

Not yet available for purchase.

Demos are coming soon.

Pro Red Team is still being finalized, and services are not yet available for purchase. More information will be added to this page as we get closer to launch.