TriNetra Platform · Offensive Testing

Break your AI assistant before someone else does.

Your customer-facing AI assistant isn't one thing to test — it's an LLM endpoint, a retrieval pipeline pulling from your own data, and a tool-calling agent that can actually do things on a user's behalf. AI Red Teaming scopes and attacks all three layers separately, then tells you exactly how far a compromise would have gone.

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AECM Assist — Pre-Launch Red Team

● COMPLETED

Multi-turn sessions run

8 pass / 1 fail

Cross-tenant isolation checks

LLM Endpoint

Azure OpenAI GPT-4o-class — direct injection, jailbreak, instruction-override

Probed

RAG Pipeline

Customer KB + transaction-history vector store, tenant-scoped — poisoned-doc retrieval

Probed

Agent Workflow

check_balance · get_recent_transactions · freeze_card · initiate_dispute · escalate_to_human

1 cross-tenant FAIL

The problem

Most AI security testing treats a product as one black box: send it some jailbreak prompts, see if it says something embarrassing, call it tested. That misses almost everything that actually goes wrong. A real AI assistant is a stack — a model endpoint, a retrieval layer that decides what context the model sees, and (increasingly) an agent workflow with real tools it's permitted to call. A prompt injection that fails against the model directly can still succeed by hiding in a document the retrieval pipeline surfaces, or by manipulating the agent into calling a tool it shouldn't with parameters it shouldn't have. Single-prompt jailbreak testing checks none of that, and if your assistant is multi-tenant, it doesn't check the one failure mode your customers would actually notice: another customer's data showing up in their session.

How AI Red Teaming answers it

AI Red Teaming scopes the LLM endpoint, the RAG pipeline, and any tool-calling agent workflow separately, the way the platform's own engagements are structured, then runs multi-turn adversarial sessions — human researchers paired with adversarial testing agents — against each layer, because the interesting failures only show up across a conversation. Multi-tenant products get explicit cross-tenant isolation checks, each returning a recorded pass or fail, and every validated finding is mapped to the frameworks your governance team already reports against.

How it actually works

Coverage you can watch build.

OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF — framework coverage tracked live as multi-turn sessions accumulate, not applied retroactively at report time.

AECM Assist — Pre-Launch Red Team

● COMPLETED

OWASP LLM Top 10

90%

MITRE ATLAS

75%

NIST AI RMF

60%

EU AI Act

50%

Prompt injection · jailbreak · cross-tenant isolation

Scoped to your actual architecture

Three layers, tested separately.

LLM ENDPOINT

The underlying model your assistant runs on — whichever foundation model or fine-tune serves the conversation. Testing probes it directly for prompt injection, jailbreak susceptibility, and instruction-override attempts, independent of everything built on top of it.

RAG PIPELINE

If your assistant answers from your own data — a knowledge base, a customer-history store, a document set — that retrieval layer is scoped and tested on its own: can a malicious document poison what gets retrieved, does the vector store leak across tenants, can retrieved context smuggle instructions past the model's guardrails.

TOOL-CALLING AGENT WORKFLOW

If the assistant can take actions — not just answer questions — every tool it's permitted to call is mapped and tested for misuse. Testing specifically tries to get the agent to invoke the wrong tool, with the wrong parameters, on the wrong account. A customer-facing banking assistant, for example, might expose tools like:

check_balanceget_recent_transactionsfreeze_cardinitiate_disputeescalate_to_human

545multi-turn sessions run

8 / 1cross-tenant checks pass / fail

Capabilities

What ships in the box.

Three layers, tested separately

LLM endpoint, RAG pipeline, and tool-calling agent workflow each get their own scope and their own attack plan — a jailbreak test against the model alone would miss a poisoned-retrieval or tool-misuse failure entirely.

Multi-turn by default

545 sessions on a representative engagement, each a full conversation, not a single prompt — because the failures that matter build across turns.

Cross-tenant isolation, scored

Every check returns a recorded pass or fail — 8/1 on a representative engagement — turning "we think tenants are isolated" into a specific, reproducible result.

Framework coverage bars, live

OWASP LLM Top 10, MITRE ATLAS, and NIST AI RMF coverage tracked as the engagement progresses, so findings arrive already in the language your compliance team reports in.

Human researchers plus adversarial agents

Agents generate volume and systematic coverage across the tool-calling surface; AI-specialized humans design the campaign and reproduce every finding before it's reported.

Works with

Stronger together.

FAQ

Common questions.

Because a retrieval-based leak or injection never touches the model's own guardrails directly — a poisoned document or a cross-tenant vector-store leak can defeat a perfectly well-aligned model. Scoping it separately is the only way to catch that class of failure.

Ship your AI assistant with evidence, not hope.

Tell SecurityBoat what you've built — the model, the retrieval layer, the tools it can call — and we'll scope a TriNetra AI Red Teaming engagement against each layer, multi-turn and framework-mapped.

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Related: Attack Surface Management · Code Security · PTaaS · Ish