AgentDojo Framework Assesses AI Agents' Adversarial Robustness
/ 3 min read
Quick take - AgentDojo is a newly developed evaluation framework designed to assess the adversarial robustness of AI agents that utilize text-based reasoning and external tool calls, addressing prompt injection attacks through a comprehensive suite of tasks and security test cases.
Fast Facts
- AgentDojo is a new evaluation framework for assessing the adversarial robustness of AI agents that combine text-based reasoning with external tool calls, focusing on prompt injection attacks.
- The initial version includes 97 realistic tasks and 629 security test cases across four environments: Workspace, Slack, Travel Agency, and e-banking.
- It evaluates AI agents using formal utility checks rather than relying on large language models, providing a more accurate assessment of attack effectiveness and defense robustness.
- The framework allows researchers to define user and attacker goals, facilitating comprehensive evaluations of AI performance under both normal and attack conditions.
- Future updates may introduce advanced attack and defense mechanisms, automate task specification, and support multimodal agents, aiming to adapt to evolving machine learning security threats.
AgentDojo: A New Framework for Evaluating AI Agent Robustness
Introduction to AgentDojo
AgentDojo is a newly introduced evaluation framework designed to assess the adversarial robustness of AI agents that integrate text-based reasoning with external tool calls. This framework addresses the growing concern of prompt injection attacks, which can manipulate AI agents into executing harmful tasks. The initial version of AgentDojo includes a comprehensive suite of 97 realistic tasks and 629 security test cases, encompassing a variety of real-world applications, such as managing emails, navigating e-banking websites, and making travel bookings.
Dynamic and Extensible Framework
The framework is dynamic and extensible, allowing researchers to design and evaluate new agent tasks, defenses, and adaptive attacks. This encourages innovative research into new design principles for reliable and robust AI agents. AgentDojo evaluates AI agents based on formal utility checks rather than relying on other large language models (LLMs) to simulate environments. This approach enables a more accurate assessment of both the effectiveness of attacks and the robustness of defenses against prompt injections.
The framework consists of various components, including environments, tools, user tasks, and injection tasks. Four distinct environments have been implemented: Workspace, Slack, Travel Agency, and e-banking. Each environment is equipped with necessary tools for task completion.
Challenges and Future Directions
Current LLMs face challenges in achieving high success rates in task performance, often falling below 66% even in the absence of attacks. Although existing prompt injection attacks can compromise some security properties of AI agents, they do not always succeed, indicating variability in how these attacks affect different tasks. The targeted attack success rate measures the effectiveness of an attack in achieving malicious objectives, revealing that more capable models may be easier to attack, which suggests an inverse scaling law.
The framework allows researchers to define user and attacker goals, facilitating a thorough assessment of AI agents’ performance under both benign conditions and in the face of attacks. Various prompt injection strategies have been tested within AgentDojo, showing differing success rates based on the strategy used and the specific tasks involved. Current defenses against prompt injections include secondary attack detectors, which have significantly reduced attack success rates.
The authors of AgentDojo acknowledge the potential for both beneficial and detrimental impacts resulting from its release as a security benchmark. Future iterations of the framework may incorporate more sophisticated attack and defense mechanisms, automate task specification, and extend support for multimodal agents. Ultimately, AgentDojo aims to provide a reliable benchmark for evaluating the robustness of AI agents against prompt injection attacks, adapting to the continuously evolving landscape of machine learning security threats.
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