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Jan 20, 2025

Scoped LLM: Enhancing Adversarial Robustness and Security Through Targeted Model Scoping

Adriano, David Baek, Erik Nordby, Emile Delcourt

Details

Details

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Even with Reinforcement Learning from Human or AI Feedback (RLHF/RLAIF)

to avoid harmful outputs, fine-tuned Large Language Models (LLMs) often present insufficient

refusals due to adversarial attacks causing them to revert to reveal harmful knowledge from

pre-training. Machine unlearning has emerged as an alternative, aiming to remove harmful

knowledge permanently, but it relies on explicitly anticipating threats, leaving models exposed

to unforeseen risks. This project introduces model scoping, a novel approach to apply a least

privilege mindset to LLM safety and limit interactions to a predefined domain. By narrowing

the model’s operational domain, model scoping reduces susceptibility to adversarial prompts

and unforeseen misuse. This strategy offers a more robust framework for safe AI deployment in

unpredictable, evolving environments.

Cite this work:

@misc {

title={

@misc {

},

author={

Adriano, David Baek, Erik Nordby, Emile Delcourt

},

date={

1/20/25

},

organization={Apart Research},

note={Research submission to the research sprint hosted by Apart.},

howpublished={https://apartresearch.com}

}

Reviewer's Comments

Reviewer's Comments

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Jan 24, 2025

Safe ai

The rapid adoption of AI in critical industries like healthcare and legal services has highlighted the urgent need for robust risk mitigation mechanisms. While domain-specific AI agents offer efficiency, they often lack transparency and accountability, raising concerns about safety, reliability, and compliance. The stakes are high, as AI failures in these sectors can lead to catastrophic outcomes, including loss of life, legal repercussions, and significant financial and reputational damage. Current solutions, such as regulatory frameworks and quality assurance protocols, provide only partial protection against the multifaceted risks associated with AI deployment. This situation underscores the necessity for an innovative approach that combines comprehensive risk assessment with financial safeguards to ensure the responsible and secure implementation of AI technologies across high-stakes industries.

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The AI Risk Management Assurance Network (AIRMAN) addresses a critical gap in AI safety: the disconnect between existing AI assurance technologies and standardized safety documentation practices. While the market shows high demand for both quality/conformity tools and observability/monitoring systems, currently used solutions operate in silos, offsetting risks of intellectual property leaks and antitrust action at the expense of risk management robustness and transparency. This fragmentation not only weakens safety practices but also exposes organizations to significant liability risks when operating without clear documentation standards and evidence of reasonable duty of care.

Our solution creates an open-source standards framework that enables collaboration and knowledge-sharing between frontier AI safety teams while protecting intellectual property and addressing antitrust concerns. By operating as an OASIS Open Project, we can provide legal protection for industry cooperation on developing integrated standards for risk management and monitoring.

The AIRMAN is unique in three ways: First, it creates a neutral, dedicated platform where competitors can collaborate on safety standards. Second, it provides technical integration layers that enable interoperability between different types of assurance tools. Third, it offers practical implementation support through templates, training programs, and mentorship systems.

The commercial viability of our solution is evidenced by strong willingness-to-pay across all major stakeholder groups for quality and conformity tools. By reducing duplication of effort in standards development and enabling economies of scale in implementation, we create clear value for participants while advancing the critical goal of AI safety.

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Jan 24, 2025

Safe ai

The rapid adoption of AI in critical industries like healthcare and legal services has highlighted the urgent need for robust risk mitigation mechanisms. While domain-specific AI agents offer efficiency, they often lack transparency and accountability, raising concerns about safety, reliability, and compliance. The stakes are high, as AI failures in these sectors can lead to catastrophic outcomes, including loss of life, legal repercussions, and significant financial and reputational damage. Current solutions, such as regulatory frameworks and quality assurance protocols, provide only partial protection against the multifaceted risks associated with AI deployment. This situation underscores the necessity for an innovative approach that combines comprehensive risk assessment with financial safeguards to ensure the responsible and secure implementation of AI technologies across high-stakes industries.

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Jan 20, 2025

AI Risk Management Assurance Network (AIRMAN)

The AI Risk Management Assurance Network (AIRMAN) addresses a critical gap in AI safety: the disconnect between existing AI assurance technologies and standardized safety documentation practices. While the market shows high demand for both quality/conformity tools and observability/monitoring systems, currently used solutions operate in silos, offsetting risks of intellectual property leaks and antitrust action at the expense of risk management robustness and transparency. This fragmentation not only weakens safety practices but also exposes organizations to significant liability risks when operating without clear documentation standards and evidence of reasonable duty of care.

Our solution creates an open-source standards framework that enables collaboration and knowledge-sharing between frontier AI safety teams while protecting intellectual property and addressing antitrust concerns. By operating as an OASIS Open Project, we can provide legal protection for industry cooperation on developing integrated standards for risk management and monitoring.

The AIRMAN is unique in three ways: First, it creates a neutral, dedicated platform where competitors can collaborate on safety standards. Second, it provides technical integration layers that enable interoperability between different types of assurance tools. Third, it offers practical implementation support through templates, training programs, and mentorship systems.

The commercial viability of our solution is evidenced by strong willingness-to-pay across all major stakeholder groups for quality and conformity tools. By reducing duplication of effort in standards development and enabling economies of scale in implementation, we create clear value for participants while advancing the critical goal of AI safety.

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Jan 24, 2025

Safe ai

The rapid adoption of AI in critical industries like healthcare and legal services has highlighted the urgent need for robust risk mitigation mechanisms. While domain-specific AI agents offer efficiency, they often lack transparency and accountability, raising concerns about safety, reliability, and compliance. The stakes are high, as AI failures in these sectors can lead to catastrophic outcomes, including loss of life, legal repercussions, and significant financial and reputational damage. Current solutions, such as regulatory frameworks and quality assurance protocols, provide only partial protection against the multifaceted risks associated with AI deployment. This situation underscores the necessity for an innovative approach that combines comprehensive risk assessment with financial safeguards to ensure the responsible and secure implementation of AI technologies across high-stakes industries.

Read More

Jan 20, 2025

AI Risk Management Assurance Network (AIRMAN)

The AI Risk Management Assurance Network (AIRMAN) addresses a critical gap in AI safety: the disconnect between existing AI assurance technologies and standardized safety documentation practices. While the market shows high demand for both quality/conformity tools and observability/monitoring systems, currently used solutions operate in silos, offsetting risks of intellectual property leaks and antitrust action at the expense of risk management robustness and transparency. This fragmentation not only weakens safety practices but also exposes organizations to significant liability risks when operating without clear documentation standards and evidence of reasonable duty of care.

Our solution creates an open-source standards framework that enables collaboration and knowledge-sharing between frontier AI safety teams while protecting intellectual property and addressing antitrust concerns. By operating as an OASIS Open Project, we can provide legal protection for industry cooperation on developing integrated standards for risk management and monitoring.

The AIRMAN is unique in three ways: First, it creates a neutral, dedicated platform where competitors can collaborate on safety standards. Second, it provides technical integration layers that enable interoperability between different types of assurance tools. Third, it offers practical implementation support through templates, training programs, and mentorship systems.

The commercial viability of our solution is evidenced by strong willingness-to-pay across all major stakeholder groups for quality and conformity tools. By reducing duplication of effort in standards development and enabling economies of scale in implementation, we create clear value for participants while advancing the critical goal of AI safety.

Read More

Jan 24, 2025

Safe ai

The rapid adoption of AI in critical industries like healthcare and legal services has highlighted the urgent need for robust risk mitigation mechanisms. While domain-specific AI agents offer efficiency, they often lack transparency and accountability, raising concerns about safety, reliability, and compliance. The stakes are high, as AI failures in these sectors can lead to catastrophic outcomes, including loss of life, legal repercussions, and significant financial and reputational damage. Current solutions, such as regulatory frameworks and quality assurance protocols, provide only partial protection against the multifaceted risks associated with AI deployment. This situation underscores the necessity for an innovative approach that combines comprehensive risk assessment with financial safeguards to ensure the responsible and secure implementation of AI technologies across high-stakes industries.

Read More

Jan 20, 2025

AI Risk Management Assurance Network (AIRMAN)

The AI Risk Management Assurance Network (AIRMAN) addresses a critical gap in AI safety: the disconnect between existing AI assurance technologies and standardized safety documentation practices. While the market shows high demand for both quality/conformity tools and observability/monitoring systems, currently used solutions operate in silos, offsetting risks of intellectual property leaks and antitrust action at the expense of risk management robustness and transparency. This fragmentation not only weakens safety practices but also exposes organizations to significant liability risks when operating without clear documentation standards and evidence of reasonable duty of care.

Our solution creates an open-source standards framework that enables collaboration and knowledge-sharing between frontier AI safety teams while protecting intellectual property and addressing antitrust concerns. By operating as an OASIS Open Project, we can provide legal protection for industry cooperation on developing integrated standards for risk management and monitoring.

The AIRMAN is unique in three ways: First, it creates a neutral, dedicated platform where competitors can collaborate on safety standards. Second, it provides technical integration layers that enable interoperability between different types of assurance tools. Third, it offers practical implementation support through templates, training programs, and mentorship systems.

The commercial viability of our solution is evidenced by strong willingness-to-pay across all major stakeholder groups for quality and conformity tools. By reducing duplication of effort in standards development and enabling economies of scale in implementation, we create clear value for participants while advancing the critical goal of AI safety.

Read More

This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.
This work was done during one weekend by research workshop participants and does not represent the work of Apart Research.