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Input validation, output filtering, and content moderation classifiers.
Also in Non-Model
Language models can be prompted to act as classifiers on inputs to and outputs of the model. The classifier large language model (LLM) can be another instance of the same model or a separate model optimized for classification tasks. For example, it has been demonstrated that prompting an LLM with simple instructions like "Does the text contain harmful content? Respond with 'Yes, this is harmful' or 'No, this is not harmful'" can achieve high accuracy and effectively reduce attack success. LLM-based classifiers could also monitor for signs of misalignment in model outputs, particularly in autonomous agent settings. In some cases, it is also possible to generate probability scores rather than binary classifications.
These LLM-based prompted classifiers are relatively simple to set up and deploy compared to other monitoring approaches, and can be easily created and customized from open-weight models. This flexibility allows developers to adjust classification thresholds based on their risk tolerance and deployment context. However, the more inputs that require assessment, the more compute and latency costs are incurred. Although costs can be reduced by using smaller models to monitor, this may lead to decreased accuracy. Additionally, LLM-based prompted classifiers are prone to circumvention if malicious prompts are separated into seemingly benign substeps or distributed across different accounts.
Reasoning
LLM-based classifier filters harmful content from model outputs before delivery.
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.
Operate and Monitor
Running, maintaining, and monitoring the AI system post-deployment
Deployer
Entity that integrates and deploys the AI system for end users
Manage
Prioritising, responding to, and mitigating AI risks
Primary
7 AI System Safety, Failures & LimitationsOther
