LLM Red Teaming Services
Most LLM red teams test the chatbot. We test the system around it.
Prompt injection, tool poisoning, permission abuse, chained across the model, the agents, and the protocols between them. Evidence at every hop. That is the engagement.
LLM red teaming is adversarial testing of systems built on large language models, run by operators who think like attackers. We probe the model, the agent tier, and the integrations for the chains a real adversary would pull, then hand you the evidence.
AI red teaming is the wedge we are loudest about right now. It is one engagement inside a full-spectrum practice: pentesting, red teaming, adversary emulation.
7-phase methodology
PIR-driven, mapped to MITRE ATT&CK, ATLAS, the OWASP LLM Top 10, and PTES.
System-level scope
Model, agents, tools, protocols, data. Tested as one surface, not five silos.
Chains, not lists
Every finding delivered as a reproducible attack chain, with evidence at each hop.
Retest included
Remediation gets proven against a live retest. Fixes are verified, not assumed.
What a Red Team Is Not
Not automated scanning.
Scanners have a real job: cheap regression over known classes, run often. They do not think like an adversary and they do not chain. A scanner report and a red team report are different products with similar names.
Not jailbreak bingo.
Evals have a real job: a fast read on the obvious failure modes. But a hundred blocked jailbreaks is a test log, not an attack path. If the report reads like blocked prompts, you bought an eval.
Not a checkbox exercise.
Audits have a real job when the auditor actually tests something. If what you need is a PDF that says testing happened, there are cheaper ways to buy one. This engagement is for teams that want to know what breaks.
The model is the best-tested part of your system
The model vendors red team their models. Benchmarks hammer the models. Evals probe the models. The model is the most scrutinized component in the stack, and it is the one attackers spend the least time on.
Attackers work the system around the model: the harness that wraps it, the tools the agent can call, the memory it reads, the protocols between services, the permissions nobody audited. Test the model without the harness and you measured the wrong thing. The harness is different at every company, which is why this work does not come in a package.
Adversaries do not file bug reports against your eval harness. They go around it.
Scope
What We Test
Four layers, tested as one surface. Each is a class of failure an attacker would reach for, not a checklist to initial.
The model layer
Prompt injection, jailbreak resistance, output handling, data extraction classes. The part everyone tests. We test it too, then keep going.
The agent and tool tier
Tool poisoning, permission abuse, confused-deputy classes, toxic flows across tool calls. This is where the real chains live, because this is where the agent is allowed to act.
The protocol layer
MCP servers, agent-to-agent protocols, function-calling surfaces, tested the way an attacker would fingerprint them. Protocol-native recon, not generic web scanning.
The data layer
RAG pipelines, memory stores, retrieval poisoning, exfiltration paths. If the agent can read it, the attacker wants to read it. We find out which one gets there first.
How we work
Rules of Engagement Are Engineering Controls
Testing systems that can take real actions needs real guardrails. Ours are enforced by the harness, not by asking nicely.
Written ROE first. Scope, authorization, and boundaries in writing before testing starts. Nothing is improvised mid-engagement.
Depth gating. The plan declares how many layers deep the test goes, and the harness enforces it. Scope creep dies in code, not in a meeting.
Scan pacing. Rate limits on tool invocation, so a runaway loop cannot burn your API budget or trip your fraud controls.
One audited chokepoint. A single supervised gate for privileged actions. Nothing irreversible happens without a human in a defined seat.
The method behind the controls is on our methodology page, and the practitioner version is in how we pentest AI agents. Guardrails live in code, not in prompts. The guardrail that is not executable is not a guardrail.
Deliverables
What You Get
Evidence you can act on and defend. Nothing in the report is a surprise we cannot reproduce.
Attack chains with evidence
Hop by hop, reproducible, each step documented. Not a finding list. A path.
Risk-ranked findings
Ranked by what an attacker can actually do with them, not by a severity label. Severity is the wrong unit of measure. Reach is the right one.
Remediation at the right layer
Fixes placed where they hold: the harness, the permission model, the protocol. Not just the prompt.
A retest that proves it
We run the chains again after remediation. What held is proven. What did not is documented, with the next move attached.
Put an adversary on it before a real one volunteers.
Tell us what your AI footprint looks like and we will scope it honestly. If the engagement you need is smaller than the one you asked for, we will tell you that too.