Cognitive Overload Attack:Prompt Injection for Long Context (2410.11272v1)

Abstract: LLMs have demonstrated remarkable capabilities in performing tasks across various domains without needing explicit retraining. This capability, known as In-Context Learning (ICL), while impressive, exposes LLMs to a variety of adversarial prompts and jailbreaks that manipulate safety-trained LLMs into generating undesired or harmful output. In this paper, we propose a novel interpretation of ICL in LLMs through the lens of cognitive neuroscience, by drawing parallels between learning in human cognition with ICL. We applied the principles of Cognitive Load Theory in LLMs and empirically validate that similar to human cognition, LLMs also suffer from cognitive overload a state where the demand on cognitive processing exceeds the available capacity of the model, leading to potential errors. Furthermore, we demonstrated how an attacker can exploit ICL to jailbreak LLMs through deliberately designed prompts that induce cognitive overload on LLMs, thereby compromising the safety mechanisms of LLMs. We empirically validate this threat model by crafting various cognitive overload prompts and show that advanced models such as GPT-4, Claude-3.5 Sonnet, Claude-3 OPUS, Llama-3-70B-Instruct, Gemini-1.0-Pro, and Gemini-1.5-Pro can be successfully jailbroken, with attack success rates of up to 99.99%. Our findings highlight critical vulnerabilities in LLMs and underscore the urgency of developing robust safeguards. We propose integrating insights from cognitive load theory into the design and evaluation of LLMs to better anticipate and mitigate the risks of adversarial attacks. By expanding our experiments to encompass a broader range of models and by highlighting vulnerabilities in LLMs' ICL, we aim to ensure the development of safer and more reliable AI systems.

• The paper demonstrates that increasing cognitive load through prompt injection leads to performance degradation in LLMs, achieving attack success rates up to 99.99%.
• The paper establishes a novel framework linking in-context learning with human cognitive load theory, revealing LLM vulnerabilities analogous to human working memory limits.
• The paper introduces innovative measurement techniques and automated algorithms to induce cognitive overload, highlighting the urgent need for more resilient LLM defense mechanisms.

Overview of the Cognitive Overload Attack in LLMs

This paper presents a detailed exploration of vulnerabilities in LLMs, particularly focusing on the concept of cognitive overload attacks as a method of prompt injection for long contexts. The research establishes a novel framework by aligning In-Context Learning (ICL) in LLMs with human cognitive processes, particularly through the lens of Cognitive Load Theory (CLT).

The authors initiate a theoretical model, suggesting that LLMs, like human cognition, are susceptible to cognitive overload—a condition where the model is overwhelmed by excessive cognitive demands, leading to errors and vulnerabilities. This paper extends its propositions by empirically validating that attacking LLMs by crafting prompts to induce cognitive overload can effectively bypass safety mechanisms, leading to a jailbreak scenario.

Key Contributions

1. ICL and Human Cognitive Learning Parallels:
   • The paper draws parallels between human cognitive processes and ICL, highlighting that LLMs experience similar types of cognitive loads—Intrinsic, Extraneous, and Germane.
   • By conceptualizing LLMs' limitations akin to the working memory constraints in humans, the paper sets the stage for further exploration of cognitive theory as applicable to AI models.
2. Empirical Validation of Cognitive Overload:
   • The research affirms that as cognitive load increases, model performance on secondary tasks diminishes, emulating effects seen in human cognition.
   • They demonstrate that SOTA models can be overwhelmed with crafted prompts, achieving high attack success rates. For instance, the paper shows that models like GPT-4 and Claude-3-Opus are susceptible to such attacks with success rates as high as 99.99%.
3. Development of Cognitive Overload Attacks:
   • By introducing the notion of deliberately designed cognitive load tasks to overwhelm LLMs, the authors propose an effective methodology to jailbreak these models.
   • The attack methodology uses an automated algorithm to iteratively increase cognitive load until the model is disoriented, leading to compromised safety responses.
4. Cognitive Load Measurement Techniques:
   • The paper proposes unique ways to assess cognitive load increases in LLMs, such as using irrelevant tasks and measuring tokens generated during a task.
   • This approach provides insights into how LLMs might allocate cognitive resources and how this can be manipulated for attacks.

Implications and Future Directions

The findings underscore critical vulnerabilities in SOTA LLMs, suggesting that as these models expand in capability and context window size, they remain inherently susceptible to cognitive overload exploits. This awareness necessitates the development of robust defenses that integrate insights from cognitive load theory into LLM design and evaluation, aiming to mitigate adversarial risks.

Further research should focus on enhancing LLM resilience to cognitive overload by optimizing safety alignments and developing models capable of efficiently managing increased cognitive demands. Additionally, exploring the theoretical underpinnings of cognitive similarity between human cognition and LLMs could enhance our understanding, paving the way for more resilient AI systems. Lastly, ensuring ethical considerations and responsible AI practices remain paramount in deploying, testing, and potentially rectifying these vulnerabilities.

In conclusion, the paper provides a significant contribution to understanding and addressing the vulnerabilities inherent in current LLM architectures through the lens of cognitive science, setting a foundation for both the theoretical exploration and practical enhancement of LLM security frameworks.