DoomArena: A framework for Testing AI Agents Against Evolving Security Threats (2504.14064v2)

Abstract: We present DoomArena, a security evaluation framework for AI agents. DoomArena is designed on three principles: 1) It is a plug-in framework and integrates easily into realistic agentic frameworks like BrowserGym (for web agents) and $\tau$-bench (for tool calling agents); 2) It is configurable and allows for detailed threat modeling, allowing configuration of specific components of the agentic framework being attackable, and specifying targets for the attacker; and 3) It is modular and decouples the development of attacks from details of the environment in which the agent is deployed, allowing for the same attacks to be applied across multiple environments. We illustrate several advantages of our framework, including the ability to adapt to new threat models and environments easily, the ability to easily combine several previously published attacks to enable comprehensive and fine-grained security testing, and the ability to analyze trade-offs between various vulnerabilities and performance. We apply DoomArena to state-of-the-art (SOTA) web and tool-calling agents and find a number of surprising results: 1) SOTA agents have varying levels of vulnerability to different threat models (malicious user vs malicious environment), and there is no Pareto dominant agent across all threat models; 2) When multiple attacks are applied to an agent, they often combine constructively; 3) Guardrail model-based defenses seem to fail, while defenses based on powerful SOTA LLMs work better. DoomArena is available at https://github.com/ServiceNow/DoomArena.

• The paper introduces DoomArena, a modular, configurable, and plugin-capable framework designed specifically for evaluating the security vulnerabilities of AI agents in dynamic environments.
• Experiments using DoomArena revealed that state-of-the-art AI agents exhibit diverse vulnerabilities, which can compound under multiple attacks, and demonstrated the inadequacy of standard defenses compared to more effective LLM-based defense mechanisms.
• The research emphasizes the critical need for frameworks like DoomArena to advance AI safety in automated systems and points towards future directions including the development of sophisticated adaptive defenses, particularly leveraging LLMs.

Overview of "DoomArena: A Framework for Testing AI Agents Against Evolving Security Threats"

The paper presents DoomArena, a robust framework explicitly designed for the security evaluation of AI agents in dynamic environments. In light of emerging security challenges facing AI agents, this framework provides a structured methodology to test and understand vulnerabilities. Particularly, the framework is developed with the intention to simulate evolving security threats that these AI agents may encounter during deployment.

Key Principles and Innovations

DoomArena is underpinned by three principal features: modularity, configurability, and plugin capability. These characteristics are paramount for its integration into existing agent-based frameworks such as BrowserGym and $\tau$-Bench. The modular nature allows for decoupling attack strategies from the specifics of the environment. This ensures a versatile platform where different attack methods can be employed across various domains without extensive redevelopment. Configurability affords users the capacity to specify threat models in detail, enabling nuanced control over which system components may be targeted or deemed vulnerable. Additionally, the framework's ability to function as a plug-in provides seamless adoption in different ecosystems, making it adaptable for diverse experimental requirements.

Experimental Results and Insights

In applying DoomArena, several compelling outcomes were observed. State-of-the-art AI agents exhibited diverse levels of vulnerability based on threat model variations, emphasizing no single agent's dominance across different attack scenarios. Notably, when subjected to multiple simultaneous attacks, vulnerabilities often compounded, revealing an unexpected layer of susceptibility within current AI agent architectures. Critically, it was noted that defenses relying on standard models like guardrails were insufficient. However, employing SOTA LLMs as a defense mechanism proved to be notably more effective, suggesting a shift in strategy might be required for real-world applications.

Implications and Future Directions

The implications of these findings suggest a critical need for the continued development of frameworks like DoomArena. Practically, understanding these vulnerabilities is crucial for advancing AI safety in automated enterprise systems, the sciences, and knowledge-based industries where AI agents are burgeoning. The theoretical framework illuminated by DoomArena is essential to establish a baseline of security expectations and to inspire future explorations into AI agent security defenses and countermeasures. Importantly, the development of adaptive defenses and thorough threat modeling remains an open field, demanding additional exploration to keep pace with evolving security dynamics.

Future advancements stemming from this research could lead to more resilient AI agents and might necessitate integrating more sophisticated, adaptive correction mechanisms that can automatically identify and mitigate newfound vulnerabilities. The introduction of LLM-based defenses suggests an area rich with potential, particularly in developing these models’ ability to preemptively recognize and counteract intricate AI exploits. These forward-looking developments will likely play a crucial role in advancing robust AI agent deployments across multiple sectors.

In summary, the research encapsulated by DoomArena elevates the discourse on AI agent security and offers a compelling directive for the trajectories of future investigations and implementations.