spostrzeżenie - Language model safety - # Red Teaming for Generative Models

Comprehensive Survey on Adversarial Attacks and Defenses for Generative Language Models

Q: How can the proposed attack taxonomy and automated red teaming framework be extended to other types of generative models beyond language models, such as image or multimodal generators?

The proposed attack taxonomy and automated red teaming framework can be extended to other types of generative models by adapting the strategies and search methods to suit the specific characteristics of those models. For image generators, the attack taxonomy can include strategies that manipulate pixel values or introduce perturbations to generate misleading or harmful images. The searchers can be designed to explore the image space efficiently and find prompts that trigger undesirable outputs from the image generator. For multimodal generators, which combine text and images, the attack taxonomy can incorporate strategies that exploit the interaction between the modalities to induce unsafe behaviors. The automated red teaming framework can integrate both text-based and image-based attacks to comprehensively evaluate the safety of multimodal generators. By considering the unique features and vulnerabilities of each type of generative model, the attack taxonomy and automated red teaming framework can be tailored to effectively assess and mitigate risks in diverse AI systems.

Q: What are the potential ethical concerns and societal implications of the red teaming techniques discussed in this survey, and how can they be addressed responsibly?

The red teaming techniques discussed in this survey raise ethical concerns related to the potential harm caused by manipulating generative models to produce harmful or biased outputs. These techniques can be misused to spread misinformation, promote unethical behaviors, or infringe on individuals' privacy. Additionally, there is a risk of unintended consequences, where the generated content may have negative impacts on individuals or communities. To address these ethical concerns responsibly, researchers and practitioners should prioritize transparency, accountability, and fairness in their red teaming practices. It is essential to clearly communicate the purpose and methodology of red teaming experiments, obtain informed consent from participants, and ensure that the generated content is used responsibly and ethically. Moreover, incorporating diverse perspectives, ethical guidelines, and oversight mechanisms can help mitigate the potential societal implications of red teaming techniques and promote the responsible use of AI technologies.

Q: Given the rapidly evolving nature of generative AI systems, what new attack vectors and defense mechanisms might emerge in the future, and how can the research community stay ahead of these developments?

As generative AI systems continue to advance, new attack vectors and defense mechanisms are likely to emerge. Attackers may exploit vulnerabilities in the training data, model architectures, or inference processes to deceive or manipulate generative models. New attack vectors could involve adversarial examples, model inversion attacks, or data poisoning to compromise the integrity and safety of AI systems. To stay ahead of these developments, the research community can proactively engage in interdisciplinary collaborations, share knowledge and best practices, and continuously evaluate and improve existing defense mechanisms. By conducting thorough risk assessments, developing robust security protocols, and fostering a culture of responsible AI research, the community can anticipate and mitigate emerging threats in generative AI systems. Additionally, ongoing education, training, and ethical guidelines can help researchers and practitioners navigate the evolving landscape of AI security and ensure the responsible development and deployment of AI technologies.

Główne pojęcia

This paper provides a comprehensive survey on the rapidly growing field of red teaming for generative language models, covering the full pipeline from risk taxonomy, attack strategies, evaluation metrics, and benchmarks to defensive approaches.

Streszczenie

The paper presents a thorough and structured review of prompt attacks on large language models (LLMs) and vision-language models (VLMs). It covers the following key aspects:

Risk Taxonomy:

Categorizes risks associated with LLMs based on policy, harm types, targets, domains, and scenarios.
Highlights the diverse nature of risks that can lead to a range of hazards.

Attack Strategies:

Proposes a comprehensive taxonomy of LLM attack strategies grounded in the inherent capabilities of models developed during pretraining and fine-tuning, such as instruction following and generation abilities.
Identifies four key areas of attack strategies: Completion Compliance, Instruction Indirection, Generalization Glide, and Model Manipulation.
Discusses techniques like affirmative suffixes, context switching, input euphemisms, output constraints, virtual simulation, and leveraging language, cipher, and personification abilities.

Automated Red Teaming:

Frames automated red-teaming methods as searching problems and decouples them into three components: state space, search goal, and search operation.
This provides a unifying perspective and unlocks a larger space for future design of automated red-teaming methods.

Evaluation:

Covers attack evaluation metrics like Attack Success Rate, attack success dimensions, and transferability.
Discusses defense evaluation, including the concept of "overkill", and various evaluator types like lexical match, prompted LLMs, specialized classifiers, and human reviewers.
Introduces different benchmark types, including comprehensive safety, specific safety concern, and attack and exploitation.

Safeguards:

Presents an overview of defensive approaches, including training-time defenses like fine-tuning and RLHF, and inference-time defenses like prompting, guardrail systems, and language model ensembles.

Emerging Areas:

Dedicates sections to discuss vulnerabilities in multimodal models and LLM-based applications.
Covers topics like multilingual attacks, overkill of harmless queries, and safety of downstream applications.

The survey aims to provide a systematic perspective on the field and unlock new areas of research.

Customize Summary

Rewrite with AI

Generate Citations

Translate Source

To Another Language

Generate MindMap

from source content

Visit Source

arxiv.org

Statystyki

"Generative models are rapidly gaining popularity and being integrated into everyday applications, raising concerns over their safety issues as various vulnerabilities are exposed."
"To gain a comprehensive understanding of potential attacks on GenAI and develop robust safeguards, researchers have conducted studies on various red-teaming strategies, automated attack approaches, and defense methods."
"Our extensive survey, which examines over 120 papers, introduces a taxonomy of fine-grained attack strategies grounded in the inherent capabilities of language models."
"We have developed the searcher framework that unifies various automatic red teaming approaches."
"Our survey covers novel areas including multimodal attacks and defenses, risks around multilingual models, overkill of harmless queries, and safety of downstream applications."

Cytaty

"Generative models are rapidly gaining popularity and being integrated into everyday applications, raising concerns over their safety issues as various vulnerabilities are exposed."
"To gain a comprehensive understanding of potential attacks on GenAI and develop robust safeguards, researchers have conducted studies on various red-teaming strategies, automated attack approaches, and defense methods."
"Our extensive survey, which examines over 120 papers, introduces a taxonomy of fine-grained attack strategies grounded in the inherent capabilities of language models."

Kluczowe wnioski z

Against The Achilles' Heel

by Lizhi Lin,Ho... o arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00629.pdf

Głębsze pytania

How can the proposed attack taxonomy and automated red teaming framework be extended to other types of generative models beyond language models, such as image or multimodal generators?

The proposed attack taxonomy and automated red teaming framework can be extended to other types of generative models by adapting the strategies and search methods to suit the specific characteristics of those models. For image generators, the attack taxonomy can include strategies that manipulate pixel values or introduce perturbations to generate misleading or harmful images. The searchers can be designed to explore the image space efficiently and find prompts that trigger undesirable outputs from the image generator.
For multimodal generators, which combine text and images, the attack taxonomy can incorporate strategies that exploit the interaction between the modalities to induce unsafe behaviors. The automated red teaming framework can integrate both text-based and image-based attacks to comprehensively evaluate the safety of multimodal generators. By considering the unique features and vulnerabilities of each type of generative model, the attack taxonomy and automated red teaming framework can be tailored to effectively assess and mitigate risks in diverse AI systems.

What are the potential ethical concerns and societal implications of the red teaming techniques discussed in this survey, and how can they be addressed responsibly?

The red teaming techniques discussed in this survey raise ethical concerns related to the potential harm caused by manipulating generative models to produce harmful or biased outputs. These techniques can be misused to spread misinformation, promote unethical behaviors, or infringe on individuals' privacy. Additionally, there is a risk of unintended consequences, where the generated content may have negative impacts on individuals or communities.
To address these ethical concerns responsibly, researchers and practitioners should prioritize transparency, accountability, and fairness in their red teaming practices. It is essential to clearly communicate the purpose and methodology of red teaming experiments, obtain informed consent from participants, and ensure that the generated content is used responsibly and ethically. Moreover, incorporating diverse perspectives, ethical guidelines, and oversight mechanisms can help mitigate the potential societal implications of red teaming techniques and promote the responsible use of AI technologies.

Given the rapidly evolving nature of generative AI systems, what new attack vectors and defense mechanisms might emerge in the future, and how can the research community stay ahead of these developments?

As generative AI systems continue to advance, new attack vectors and defense mechanisms are likely to emerge. Attackers may exploit vulnerabilities in the training data, model architectures, or inference processes to deceive or manipulate generative models. New attack vectors could involve adversarial examples, model inversion attacks, or data poisoning to compromise the integrity and safety of AI systems.
To stay ahead of these developments, the research community can proactively engage in interdisciplinary collaborations, share knowledge and best practices, and continuously evaluate and improve existing defense mechanisms. By conducting thorough risk assessments, developing robust security protocols, and fostering a culture of responsible AI research, the community can anticipate and mitigate emerging threats in generative AI systems. Additionally, ongoing education, training, and ethical guidelines can help researchers and practitioners navigate the evolving landscape of AI security and ensure the responsible development and deployment of AI technologies.