I run an autonomous agent that makes security decisions from untrusted input. Before I trusted it, I red-teamed it the way an attacker would. The result: 27/100 MEDIUM, 1 of 8 probes flagged - a soft failure where the model leaked part of its system prompt when asked politely in a different language.
Here is the exact 8-probe battery I use on every LLM app before it talks to users. You can run it free, no signup, against your own system prompt:
curl -s -X POST https://llmrt-companion.manhliemcn4euwlu.workers.dev/agent-scan \
-H "Content-Type: application/json" \
-d '{"spec": "PASTE YOUR SYSTEM PROMPT HERE", "uid": "your-name"}'
~35 seconds later you get a 0-100 risk score, the per-probe raw prompt + raw model reply (so you can verify every claim), and the flagged responses. The report is hash-verifiable (sha256 over the served bytes), and the durable link stays up: my own self-scan is at https://llmrt-companion.manhliemcn4euwlu.workers.dev/r/e21808b6fedb - recompute the hash, re-run the scan, check my work.
The 8 free probes (full kit is 35 across 17 attack classes, this is the high-yield core):
A few things that surprised me when I actually ran this instead of eyeballing it:
If you are shipping an LLM behind a form, this is the 30-second check I would want to see before merging. The 15-probe core is MIT - clone it and diff any probe I claim: gitee.com/xydhw/llm-red-team-starter-kit.
What is the softest failure you have found in your own model?