AI Security Testing // Offensive
Every model, agent and copilot you ship is a new way in. Prompt injection, data exfiltration and model abuse never show up in a code review, so AI security testing is the only thing that finds them. We attack the way a real intruder would, then hand you proof.
Enterprise-grade cybersecurity, within reach.
The New Surface
Your firewall never saw this coming. The moment a model takes untrusted input and reaches real tools, it becomes the softest target you own. Three classes of attack land first.
We smuggle instructions through user input, documents and tool output until your model follows ours instead of yours. Direct and indirect, including payloads it reads but never shows a human.
Outcome: the exact inputs that hijack your model, with the fix.
We coax the system into leaking what it should never reveal: other users’ data, secrets in the prompt, training material and the keys to the tools behind it.
Outcome: a ranked list of what leaks, and through which path.
We push past your guardrails to make the model act outside its remit: jailbreaks, unsafe actions through connected tools and quiet abuse of the agent’s permissions.
Outcome: the guardrails that hold, and the ones that do not.
What We Test
We do not stop at the chat box. We test the whole chain, because attackers chain it: one weak prompt into one over-permissioned tool is the whole breach.
The AI-Era Edge
Attackers already run AI-augmented tooling against you. So do we. But automation finds noise; an operator finds the breach. Every finding we hand you is verified by a CREST-certified human first.
AI sharpens the tradecraft; it never sets the price. The savings come from a lean team, which is how enterprise-grade cybersecurity stays within reach.
Before you ask
Every assessment starts where an attacker would: outside, watching, looking for the one door left ajar. We find it, then we show you the walk-through.
AI security is the practice of protecting AI systems, the models, agents and copilots plus the data and tools around them, from attack and misuse. It covers the AI-specific risks traditional security misses: prompt injection, data exfiltration through the model, tool and agent abuse, and model theft. In practice it means testing an AI system adversarially before and after deployment, then closing the gaps.
AI security testing is the hands-on part: attacking a deployed AI system to find exploitable weaknesses. It ranges from a structured AI penetration test against the OWASP LLM Top 10 to open-ended AI red teaming. The output is the attack paths that actually work and the guardrails that close them, verified by a person rather than a scanner.
AI safety is about a model behaving well and avoiding harmful outputs. AI security is about stopping an attacker from abusing the system: injecting instructions, stealing data, or forcing an agent to misuse the tools it holds. Red Team Partners does security, the adversarial side, not model safety tuning.
Start by mapping what the AI can read, the tools it can call, and the permissions it holds. Then test each against the known weakness classes, prompt injection, data exfiltration, excessive agency and the rest, and fix what opens. The riskiest systems are the ones that read untrusted content and can then take an action.
It scales with the system's reach: a single LLM feature is a smaller engagement than a fleet of agents with tool access. We start with a free audit that shows your exposure, then a fixed price to test it. You get proven findings and the fixes, not a scanner report you have to triage.
Get ahead of it
Tell us what you have shipped: the model, the agent, the copilot, the systems around it. We scope the right test on a short call and show you where an attacker would start. No deck, no sales theatre, just the doors before anyone else finds them.
We hack it before they do.
