Run AgentDojo benchmark environments to evaluate prompt-injection attacks and defenses against LLM agents before trusting them with real tools or data.
Scanned 9/2/2026
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---
name: "Stress-test agent defenses with AgentDojo"
slug: "stress-test-agent-defenses-with-agentdojo"
description: "Run AgentDojo benchmark environments to evaluate prompt-injection attacks and defenses against LLM agents before trusting them with real tools or data."
github_stars: 619
verification: "security_reviewed"
source: "https://github.com/ethz-spylab/agentdojo"
author: "ETH Zurich SPY Lab"
publisher_type: "academic open-source project"
category: "Security & Verification"
framework: "Multi-Framework"
tool_ecosystem:
github_repo: "ethz-spylab/agentdojo"
github_stars: 619
---
# Stress-test agent defenses with AgentDojo
Run AgentDojo benchmark environments to evaluate prompt-injection attacks and defenses against LLM agents before trusting them with real tools or data.
## Prerequisites
Python 3.10+, agentdojo package, model provider credentials for the target pipeline, optional transformers extra for prompt-injection detector workflows
## Installation
Use the upstream install or setup path that matches your environment:
- pip install agentdojo
- pip install "agentdojo[transformers]"
Requirements and caveats from upstream:
-   
- python -m agentdojo.scripts.benchmark -s workspace -ut user_task_0 \
- python -m agentdojo.scripts.benchmark --model gpt-4o-2024-05-13 \
Basic usage or getting-started notes:
- [!IMPORTANT]
- Note that the API of the package is still under development and might change in the future.
- If you want to use the prompt injection detector, you need to install the transformers extra:
- Source: https://github.com/ethz-spylab/agentdojo
- Extracted from upstream docs: https://raw.githubusercontent.com/ethz-spylab/agentdojo/HEAD/README.md
## Documentation
- https://agentdojo.spylab.ai/
## Source
- [Agent Skill Exchange](https://agentskillexchange.com/skills/stress-test-agent-defenses-with-agentdojo/)
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