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Examining Automated Adversarial Attacks on Large Language Models and Their Implications for Safety, (from page 20230819.)

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Summary

This research from Carnegie Mellon University and others examines the safety of large language models (LLMs) like ChatGPT and Bard. It reveals that automated adversarial attacks can be generated to induce harmful responses from these models, even after extensive fine-tuning to prevent such content. Unlike manual jailbreaks, these automated methods can create numerous attack vectors that may affect both open-source and closed-source LLMs. The study emphasizes the difficulty of fully addressing these vulnerabilities, which parallels challenges faced in computer vision. The researchers aim to highlight the potential dangers of automated attacks as LLMs become more autonomous and integrated into various applications. Despite disclosing their findings to the companies involved, the underlying challenge of adversarial attacks on LLMs remains unresolved, prompting a call for further research in this area.

Signals

name description change 10-year driving-force relevancy
Automated Adversarial Attacks on LLMs Automated techniques to generate adversarial inputs for LLMs that can elicit harmful content. Shifting from manual jailbreaks to automated methods for exploiting LLM vulnerabilities. In 10 years, LLMs may struggle with inherent vulnerabilities, impacting their trustworthiness and usage. The increasing reliance on LLMs for autonomous decision-making raises concerns about safety and reliability. 5
Widespread Adoption of LLMs Growing use of LLMs in various applications, including autonomous systems. Transitioning from experimental use to mainstream adoption of LLMs in critical systems. LLMs may be integral to daily life, influencing decisions in healthcare, finance, and more. Demand for automation and AI-assisted decision-making in many sectors drives LLM adoption. 4
Difficulty in Addressing Adversarial Vulnerabilities Persistent challenges in fully patching vulnerabilities in LLMs against adversarial inputs. From manageable vulnerabilities to potentially unpatchable risks in LLM safety. LLMs may be limited in their applications due to ongoing security concerns. The complexity of deep learning models makes comprehensive safety assurances challenging. 5
Public Awareness of LLM Risks Growing recognition of the risks associated with using LLMs, including harmful content generation. Awareness shifting from ignorance to recognition of inherent risks in LLM deployment. Increased regulatory scrutiny and user caution regarding LLM applications. High-profile incidents of LLM misuse raise public and organizational awareness of risks. 4

Concerns

name description relevancy
Automated Adversarial Attacks The automation of adversarial attacks on LLMs could lead to widespread misuse, allowing harmful content generation with minimal effort. 5
Unpatchable Vulnerabilities The possibility that certain vulnerabilities in deep learning models may never be fully resolved raises serious safety concerns. 5
Increased Autonomy of AI Systems As LLMs are used more autonomously, the risks associated with their potential harmful outputs may significantly increase. 5
Propagation of Harmful Content With techniques to generate harmful content disseminated, the risk of such content becoming prevalent grows higher. 4
Insufficient Safety Measures Existing safety measures may be ineffective against new, automated methods of breaching LLM content filters. 4
Autonomous Decision-making based on LLMs Reliance on LLMs for autonomous decision-making could lead to dangerous outcomes if these models produce harmful content. 5

Behaviors

name description relevancy
Automated Adversarial Attacks The ability to automatically generate adversarial queries that exploit LLM vulnerabilities, leading to harmful responses. 5
Unintended Content Generation The phenomenon where LLMs produce harmful content despite fine-tuning efforts due to adversarial inputs. 5
Increased Reliance on Autonomous AI The growing trend of using LLMs in autonomous systems, raising concerns about safety and ethical implications. 4
Public Awareness of AI Risks Heightened awareness and discourse around the risks and limitations of LLMs as they become more widely adopted. 4
Iterative Jailbreak Evolution The continuous evolution of jailbreak techniques to exploit LLMs, indicating a persistent security challenge. 4

Technologies

name description relevancy
Adversarial Attacks on LLMs Automated construction of adversarial attacks that induce harmful responses from large language models, raising safety concerns. 5
Large Language Models (LLMs) Safety Research Systematic study of safety mechanisms in LLMs to prevent harmful content generation, including automated attack methodologies. 4
Autonomous AI Systems Use of LLMs in autonomous systems that act based on user queries, highlighting risks of harmful content generation. 4
Automated Jailbreak Techniques Methods to bypass safety protocols in LLMs using automated techniques, increasing the potential for misuse. 5

Issues

name description relevancy
Automated Adversarial Attacks on LLMs The potential for automated systems to exploit vulnerabilities in language models raises significant safety concerns as LLMs become more widely used. 5
Inevitability of Deep Learning Threats The difficulty in fully patching adversarial attacks may suggest inherent vulnerabilities in deep learning models, impacting their reliability. 4
Autonomous Actions Based on LLMs As LLMs are integrated into systems that take autonomous actions, the risks associated with harmful content generation become more substantial. 5
Public Awareness of LLM Vulnerabilities Increased awareness and understanding of the vulnerabilities in LLMs is crucial as their use expands, to mitigate potential harms. 4
Need for Research on LLM Safety The need for ongoing research into the safety and security of LLMs is emphasized to address emerging threats effectively. 5