A Trembling House of Cards? Mapping Adversarial Attacks against Language Agents (2402.10196v1)

Abstract: Language agents powered by LLMs have seen exploding development. Their capability of using language as a vehicle for thought and communication lends an incredible level of flexibility and versatility. People have quickly capitalized on this capability to connect LLMs to a wide range of external components and environments: databases, tools, the Internet, robotic embodiment, etc. Many believe an unprecedentedly powerful automation technology is emerging. However, new automation technologies come with new safety risks, especially for intricate systems like language agents. There is a surprisingly large gap between the speed and scale of their development and deployment and our understanding of their safety risks. Are we building a house of cards? In this position paper, we present the first systematic effort in mapping adversarial attacks against language agents. We first present a unified conceptual framework for agents with three major components: Perception, Brain, and Action. Under this framework, we present a comprehensive discussion and propose 12 potential attack scenarios against different components of an agent, covering different attack strategies (e.g., input manipulation, adversarial demonstrations, jailbreaking, backdoors). We also draw connections to successful attack strategies previously applied to LLMs. We emphasize the urgency to gain a thorough understanding of language agent risks before their widespread deployment.

• The paper presents a framework categorizing language agents into perception, brain, and action to systematically analyze adversarial threats.
• It identifies 12 potential attack scenarios, illustrating how input manipulation, cognitive misdirection, and tool misuse expose vulnerabilities.
• The study underscores the need for robust defense strategies and further research to enhance the security of AI language agents.

Comprehensive Examination of Adversarial Attacks on Language Agents

Introduction to Language Agent Vulnerabilities

In their scholarly examination, Mo et al. explore the intricate field of language agents, which have profoundly transformed how artificial intelligence interacts through language. The researchers' substantial contribution highlights the flexibility and prowess of language agents towards reasoning, planning, and executing tasks across various domains. Despite their potential, the paper casts light on a pivotal concern: the susceptibility of these agents to adversarial attacks. As language agents begin to interface with external components, their exposure to multifaceted risks escalates. The paper meticulously outlines the potential adversarial threats against language agents, providing a framework for understanding the full gamut of vulnerabilities.

A Unified Conceptual Framework

Mo et al. propose a comprehensive framework categorizing language agent functionalities into Perception, Brain, and Action components. This subdivision not only aligns with human cognitive processes but also facilitates a detailed analysis of potential adversarial attacks.

• Perception: This involves the processing of textual, visual, and auditory inputs, which are foundational to a language agent's interaction with its environment.
• Brain: At this core lies the cognitive aspect, where reasoning and planning occur. It includes working memory and long-term memory, emulating the cognitive processes of decision-making.
• Action: The final component translates cognitive decisions into actions, utilizing tools or APIs to interact with external databases or execute tasks in digital or physical realms.

Potential Adversarial Attacks

The paper postulates and discusses twelve hypothetical scenarios demonstrating how adversaries could potentially exploit vulnerabilities across the Perception, Brain, and Action components.

• Perception: Attackers could manipulate inputs to alter an agent's understanding or actions, such as embedding misleading signals in visual inputs.
• Brain: Threats here include altering the agent's reasoning and planning mechanisms, for instance, by providing deceitful feedback or malicious demonstrations that could sway decision-making processes.
• Action: In this domain, vulnerabilities could lead language agents to erroneously utilize external tools or execute unintended actions, highlighting the risks incorporated from interacting with external systems.

Implications and Future Directions

The paper strongly emphasizes the importance of recognizing and addressing the security risks associated with language agents. With the pioneering exploration of adversarial attacks tailored to the composite structure of language agents, Mo et al. aim to awaken the AI research community to the pressing need for robust defenses. The proposed framework not only serves as a tool for dissecting and understanding attack vectors but also forms the foundation for developing mitigative strategies to counteract these vulnerabilities.

Furthermore, the dialogue around future advancements in AI and language agents becomes greatly enriched by acknowledging these potential adversarial threats. As the deployment of language agents becomes more pervasive in practical applications, the insights from this investigation into their vulnerabilities form a crucial cornerstone for fostering both innovation and security in AI development.

Concluding Remarks

The meticulous investigation by Mo et al. into adversarial attacks against language agents signifies a pivotal step towards comprehending and enhancing the security of these sophisticated AI systems. By delineating a landscape rife with potential threats, the paper emphatically calls for continued research into fortified defenses, aiming to safeguard the future of AI from the fragility posed by adversarial exploits. In the broader context of AI development, it underscores a collective responsibility to navigate the intricate balance between innovation and security.