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.
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 works
Scoped to your actual architecture
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.
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.
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_humanGovernance
Framework coverage, tracked live
Injection, jailbreak, and instruction-override categories are exercised first and most heavily on a representative engagement.
Adversarial-ML technique categories build out as multi-turn sessions accumulate.
Governance-facing risk-management categories are tracked through the engagement's later phases.
Representative coverage from a live engagement — tracked as testing progresses, not applied retroactively at report time. Shown here as qualitative state, not a specific score, because coverage moves engagement to engagement.
Capabilities
What ships in the box
Three layers, tested separately
Multi-turn by default
Cross-tenant isolation, scored
Framework coverage bars, live
Human researchers plus adversarial agents
Works with
Stronger together
Attack Surface Management
Code Security
PTaaS
Ish
FAQ
Common questions
Why test the RAG pipeline separately from the model?
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.
What does a tool-calling agent test actually look for?
Whether the agent can be manipulated into invoking a tool outside its intended use — the wrong tool, the wrong parameters, or against the wrong account — using multi-turn conversation to build the context needed to trigger the misuse.
What happens with a cross-tenant failure?
It's recorded as a failed check on the pass/fail tally, treated as a blocking finding, and reported with a reproducible attack trace — this is the failure mode that gets escalated fastest, because it's the one your customers would notice first.
Which frameworks do findings map to?
OWASP LLM Top 10, MITRE ATLAS, and NIST AI RMF, tracked as live coverage through the engagement — not applied retroactively at report time.
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.
Related: Attack Surface Management · Code Security · PTaaS · Ish