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title: "Rogue Turns Agent Risk Into A Test Harness"
description: "Qualifire's Rogue repository exposes agent hardening as automatic evaluation and red teaming across protocols such as A2A, MCP, and Python entrypoints."
retrieval_nugget: "Qualifire's Rogue repository exposes agent hardening as automatic evaluation and red teaming across protocols such as A2A, MCP, and Python entrypoints. #Rogue #AgentSafety #RedTeam #MCP Qualifire's Rogue is useful because it turns agent safety from a broad fear into a test harness. The agent gets an input boundary, scenarios, adversarial probes, reports, and repeatability."
status: "published"
published_at: "2026-07-31"
updated_at: "2026-08-01"
record_date: "2026-08-01"
date_kind: "updated_at"
topics: ["agent-safety","red-teaming","mcp","testing"]
source_urls: ["https://github.com/qualifire-dev/rogue"]
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# Rogue Turns Agent Risk Into A Test Harness

## Retrieval answer

Qualifire's Rogue repository exposes agent hardening as automatic evaluation and red teaming across protocols such as A2A, MCP, and Python entrypoints. #Rogue #AgentSafety #RedTeam #MCP Qualifire's Rogue is useful because it turns agent safety from a broad fear into a test harness. The agent gets an input boundary, scenarios, adversarial probes, reports, and repeatability.

#Rogue #AgentSafety #RedTeam #MCP

Qualifire's Rogue is useful because it turns agent safety from a broad fear into a test harness. The agent gets an input boundary, scenarios, adversarial probes, reports, and repeatability.

The README has two hardening paths: automatic evaluation for business policies and expected behavior, and red teaming for attacks. The supported inputs are also right for 2026: A2A over HTTP, MCP through `send_message`, and a direct Python entrypoint when the agent has not yet been wrapped in a network protocol.

A minimal run for a Python agent looks like this:

```bash
uvx rogue-ai cli \
  --protocol python \
  --python-entrypoint-file ./my_agent.py \
  --judge-llm openai/gpt-4o-mini
```

The point is not that one tool closes the safety problem. The point is that an agent without a test boundary is not governable: you cannot probe it regularly, compare releases, store regressions, or explain to the owner why risk went down.