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Service — AI Red Teaming

AI & LLM Red Teaming

A content filter is not a security control. We test what your model, your agent, and the tools behind it can actually be talked into doing — with the same persistence a motivated attacker would bring.

AI Security[ LLM ]

Why it matters

The model isn't the vulnerability. The system around it is.

Language models follow instructions — including instructions hidden in a support ticket, a PDF, a web page, or a calendar invite your assistant was asked to summarize. Once a model can call tools, query a database, or send an email, a successful injection stops being a bad answer and starts being an action taken on your infrastructure, with your credentials.

Where it applies

If a model can read untrusted input or take an action — it needs testing.

  • Customer-facing chatbots and support assistants
  • RAG systems querying internal or customer documents
  • Autonomous agents with tool, API, or shell access
  • AI copilots embedded in an existing SaaS product
  • Code-generation and developer assistants
  • Document and email processing pipelines
  • Voice and multimodal assistants
  • Pre-launch validation before an AI feature ships

Our methodology

We attack the whole system, not just the prompt box.

Running a public jailbreak list against your chatbot is not a red team. We map the model's real trust boundaries — every place untrusted text enters and every action the system can take — then attack across them.

  1. 01

    AI Threat Modeling

    Mapping every untrusted input path, every tool the model can invoke, and every data source it's allowed to reach.

  2. 02

    Direct Prompt Injection & Jailbreaks

    Systematically testing guardrail bypass, system-prompt extraction, and role manipulation — including multi-turn and encoded payloads.

  3. 03

    Indirect Injection via Untrusted Content

    Planting instructions in the documents, tickets, web pages, and emails your system ingests — the attack path most teams never test.

  4. 04

    Tool & Agent Abuse Testing

    Testing whether the model can be steered into invoking tools with attacker-chosen arguments, chaining calls, or exceeding its intended authority.

  5. 05

    Data Exfiltration & RAG Boundary Testing

    Verifying whether one tenant, user, or role can pull retrieved context, embeddings, or documents belonging to another.

  6. 06

    Reporting & Guardrail Hardening

    Reproducible prompt chains for every finding, plus concrete architectural fixes — not just "add it to the system prompt."

Standards & compliance

Mapped to the AI security frameworks regulators and customers now ask for.

AI security is moving from best practice to requirement. Our testing and reporting align with the frameworks your customers' security reviews and your auditors are starting to require.

  • OWASP Top 10 for LLM Applications
  • MITRE ATLAS
  • NIST AI RMF
  • Google SAIF
  • ISO/IEC 42001
  • EU AI Act Readiness

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Want to know what your AI assistant can be talked into?

Tell us what your model has access to, and we'll scope a red-team engagement around it.

Request an AI Assessmentconnect@uncrypt.net

Within one business day · NDA on request