This page is still being polished. If you have thoughts, please share them via the feedback form.
Data on this page is preliminary and may change. Please do not share or cite these figures publicly.
Foundational safety research, theoretical understanding, and scientific inquiry informing AI development.
Also in Engineering & Development
This report outlines current approaches to frontier mitigations, but as capabilities advance and our understanding of effective safeguards improves, developer approaches will be updated accordingly. Areas of continued work include: ● Improving Mitigation Design and Implementation. Developing more robust safety mechanisms that resist circumvention attempts; exploring new approaches to capability limitation and behavioral alignment that remain effective as models scale; and, establishing best practices for implementing controls across different deployment contexts – from API services to open-weight releases. ● Advancing Assessment and Validation Methods. Developing standardized approaches for testing individual controls and integrated systems; creating evaluation methods that remain effective as models acquire new capabilities like deception; and, establishing empirical validation of mitigation effectiveness against realistic threats. ● Strengthening Security and Resilience. Developing infrastructure security measures proportional to threat actors' or models’ capabilities; creating safety mechanisms that persist despite modification attempts; and, building assessment frameworks that account for how threats might manifest through multiple attack vectors simultaneously. Future research should focus on developing evidence-based approaches for the continuous improvement of mitigation strategies, helping them remain responsive to both emerging model capabilities and evolving threat landscapes as frontier AI systems continue to advance.
Reasoning
Mitigation definition and evidence placeholders prevent identifying focal activity or mechanism.
Capability Limitation Mitigations
Capability limitation mitigations aim to prevent models from possessing knowledge or abilities that could enable harm. These methods alter the model’s weights or training process, so that it cannot assist with harmful actions when prompted by humans or autonomously pursue harmful objectives.
1.1.3 Capability ModificationCapability Limitation Mitigations > Data Filtering
Data filtering involves removing content from training datasets that could lead to dual-use or potentially harmful capabilities. Developers can use several methods: automated classifiers to identify and remove content related to weapons development, detailed attack methodologies, or other high-risk domains; keyword-based filters to exclude documents containing specific terminology or instructions of concern; and machine learning models trained to recognize subtle patterns in content that might contribute to dangerous capabilities.
1.1.1 Training DataCapability Limitation Mitigations > Exploratory Methods
Beyond data filtering, researchers are investigating additional capability limitation approaches
1.1.3 Capability ModificationCapability Limitation Mitigations
Capability limitation mitigations aim to prevent models from possessing knowledge or abilities that could enable harm. These methods alter the model's weights or training process, so that it cannot assist with harmful actions when prompted by humans or autonomously pursue harmful objectives. However, the effectiveness of these mitigations is an active area of research, and they can currently be circumvented if dual-use knowledge (knowledge that has both benign and harmful applications) is added in the context window during inference or fine-tuning.
1.1.3 Capability ModificationCapability Limitation Mitigations > 2.1 Data Filtering
Data filtering involves removing content from training datasets that could lead to dual-use or potentially harmful capabilities. Developers can use several methods: automated classifiers to identify and remove content related to weapons development, detailed attack methodologies, or other high-risk domains; keyword-based filters to exclude documents containing specific terminology or instructions of concern; and machine learning models trained to recognize subtle patterns in content that might contribute to dangerous capabilities.
1.1.1 Training DataCapability Limitation Mitigations > 2.2 Exploratory Methods
Beyond data filtering, researchers are investigating additional capability limitation approaches. However, these methods face technical challenges, and their effectiveness remains uncertain. ● Model distillation could create specialized versions of frontier models with capabilities limited to specific domains. For example, a model could excel at medical diagnosis while lacking knowledge needed for biological weapons development. While the capability limitations may be more fundamental than post-hoc safety training, it remains unclear how effectively this approach prevents harmful capabilities from being reconstructed. Additionally, multiple specialized models would be needed to cover various use cases, increasing development and maintenance costs. ● Targeted unlearning attempts to remove specific dangerous capabilities from models after initial training, offering a more precise alternative to full retraining. Possible approaches include fine-tuning on datasets to overwrite specific knowledge while preserving general capabilities, or modifying how models internally structure and access particular information. However, these methods may be reversible with relatively modest effort – restoring "unlearned" capabilities through targeted fine-tuning with small datasets. Models may also regenerate removed knowledge by inferring from adjacent information that remains accessible. While research continues on these approaches, developers currently rely more heavily on post-deployment mitigations that can be more reliably implemented and assessed.
1.1.3 Capability ModificationFrontier Mitigations
Frontier Model Forum (2025)
Frontier mitigations are protective measures implemented on frontier models, with the goal of reducing the risk of potential high-severity harms, especially those related to national security and public safety, that could arise from their advanced capabilities. This report discusses emerging industry practices for implementing and assessing frontier mitigations. It focuses on mitigations for managing risks in three primary domains: chemical, biological, radiological and nuclear (CBRN) information threats; advanced cyber threats; and advanced autonomous behavior threats. Given the nascent state of frontier mitigations, this report describes the range of controls and mitigation strategies being employed or researched by Frontier Model Forum members and documents the known limitations of these approaches.
Other (outside lifecycle)
Outside the standard AI system lifecycle
Developer
Entity that creates, trains, or modifies the AI system
Other
Risk management function not captured by the standard AIRM categories
