Coercing LLMs to Do and Reveal (Almost) Anything with Jonas Geiping - #678
Apr 1, 2024
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Jonas Geiping, a research group leader at the ELLIS Institute and Max Planck Institute, sheds light on his groundbreaking work on coercing large language models (LLMs). He discusses the alarming potential for LLMs to engage in harmful actions when misused. The conversation dives into the evolving landscape of AI security, exploring adversarial attacks and the significance of open models for research. They also touch on the complexities of input optimization and the balance between safeguarding models while maintaining their functionality.
Understanding the risks of deploying LLM agents in real-world interactions is crucial due to their vulnerability to coercion and unintended actions.
The interchangeability of attacks across models highlights the need for robust defense strategies that transcend model sources and attack methods.
The evolving landscape of LLM security necessitates a multifaceted approach involving heuristic methods, guard models, and ongoing research into optimization algorithms.
Deep dives
Research Origins and Importance of LLM Security
The podcast episode delves into the origins of research into large language model (LLM) security. It highlights the evolving landscape from previous work restricted to image attacks to the emergence of attacks on language models. Notably, developments like Ennizo's attack broadened the field, leading to a surge of related attacks. This shift underscores the current pivotal space of debating attack reliability and speed of execution.
Security Implications and Future Outlook
The episode stresses the security implications of LLMs as agents in near-future agentic systems. It caution against using current tech for interaction with the external world due to the potential risks. The discussion revolves around the vulnerability of LLMs to being coerced into unintended actions by exploiting their ability to take actions in various contexts, indicating a need for cautious implementation and clear understanding of potential risks.
Adversarial Attacks and Open Source Models
The podcast explores how attacks developed on open-source models such as the Llama derivatives can transfer to black box models. The discussion touches on the similarities in attack mechanisms across models owing to their architectural and data training similarities. Additionally, the mention of attacks utilizing generative algorithms and query-based methods emphasizes the need for robust defense strategies irrespective of the model source.
Defense Strategies and Optimizations
The episode touches on the evolving landscape of defense strategies against adversarial attacks on LLMs, mentioning heuristic methods like filtering for input complexity to make attacks harder. It further discusses the use of guard models for detection of adversarial inputs, while acknowledging the challenges of fooling both the detector and the model simultaneously. Moreover, the conversation hints at the ongoing research into optimization algorithms and the complexity of ensuring model safety amidst potential vulnerabilities.
Future Adventures in LLM Security
The episode wraps up with a speculative discussion on the potential future of LLM security, contemplating the role of automated attacks versus manual red teaming. It highlights the importance of understanding persuasive attacks based on human psychology, which could influence how well attackers can exploit LLM vulnerabilities. The conversation also delves into the societal implications and considerations around securing wide-scale deployment of LLMs in various applications, foreseeing a complex and evolving landscape of security measures and potential threats.
The Intersection of Social Engineering and Technical Attacks
The episode reflects on the parallels between social engineering and technical attacks on LLMs, suggesting that many attacks lean towards psychological persuasion rather than complex technical methods. It delves into the potential impact of social engineering-based attacks on enhancing attackers' success rates and the broader implications for the security of LLMs in practical scenarios. The narrative shifts towards a speculative outlook on the potential outcomes arising from the interplay between societal trust, LLM vulnerabilities, and evolving attack methodologies.
Today we're joined by Jonas Geiping, a research group leader at the ELLIS Institute, to explore his paper: "Coercing LLMs to Do and Reveal (Almost) Anything". Jonas explains how neural networks can be exploited, highlighting the risk of deploying LLM agents that interact with the real world. We discuss the role of open models in enabling security research, the challenges of optimizing over certain constraints, and the ongoing difficulties in achieving robustness in neural networks. Finally, we delve into the future of AI security, and the need for a better approach to mitigate the risks posed by optimized adversarial attacks.
The complete show notes for this episode can be found at twimlai.com/go/678.
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