SentinelAgentic AI Red-Team Auditor

AI that red-teams AI

Sentinel adversarially probes a target agent for jailbreaks, guardrail bypasses, data leaks and unsafe tool calls — then produces a severity-scored report with a minimized prompt you can rerun yourself.

Get to know it

Every agent that ships to the public should have been broken in private first - by something that improvises, not by a checklist somebody last updated two model generations ago.

So Sentinel runs the engagement you would run yourself if you had the week: it probes, escalates, proves the finding reproduces, and hands back the shortest prompt that still works.

Find out how your agent breaks before somebody else does.

Refusal training, system prompts and output filters hold against the attacks somebody already thought of, but these are all static defences.

They need an adversary.

The adversarial loop

A filter answers one question, and answers it the same way however many times it is asked. This decides what to do next from what the target just did - press the same seam, pivot to another category, or drop the thread and move on.

Every tool call the target attempts is intercepted on the way through and logged whether or not it runs. A leak is as often there as in anything the target said.

Sentinel goes after your agent the way somebody eventually will.

Jailbreaks, guardrail bypasses, data leaks and unsafe tool calls — probed under a scope you signed, on a budget you capped.

Start an audit

Finding confirmed

now

rag_context_poisoning reproduced on every rerun. Severity 8.4, critical.

Every agent needs an adversary. Not every team has the week.

What Sentinel goes looking for

Authorize a scope

  • Tool calls it was never meant to make

    tool_parameter_hijacking

  • A planted document that supersedes your policy

    rag_context_poisoning

  • Ten turns of patient erosion

    multiturn_erosion