Source URL: https://cloud.google.com/blog/products/ai-machine-learning/enhance-gemini-model-security-with-content-filters-and-system-instructions/
Source: Cloud Blog
Title: Enhance Gemini model security with content filters and system instructions
Feedly Summary: As organizations rush to adopt generative AI-driven chatbots and agents, it’s important to reduce the risk of exposure to threat actors who force AI models to create harmful content.
We want to highlight two powerful capabilities of Vertex AI that can help manage this risk — content filters and system instructions. Today, we’ll show how you can use them to ensure consistent and trustworthy interactions.
Content filters: Post-response defenses
By analyzing generated text and blocking responses that trigger specific criteria, content filters can help block the output of harmful content. They function independently from Gemini models as part of a layered defense against threat actors who attempt to jailbreak the model.
Gemini models on Vertex AI use two types of content filters:
Non-configurable safety filters automatically block outputs containing prohibited content, such as child sexual abuse material (CSAM) and personally identifiable information (PII).
Configurable content filters allow you to define blocking thresholds in four harm categories (hate speech, harassment, sexually explicit, and dangerous content,) based on probability and severity scores. These filters are default off but you can configure them according to your needs.
It’s important to note that, like any automated system, these filters can occasionally produce false positives, incorrectly flagging benign content. This can negatively impact user experience, particularly in conversational settings. System instructions (below) can help mitigate some of these limitations.
aside_block
System instructions: Proactive model steering for custom safety
System instructions for Gemini models in Vertex AI provide direct guidance to the model on how to behave and what type of content to generate. By providing specific instructions, you can proactively steer the model away from generating undesirable content to meet your organization’s unique needs.
You can craft system instructions to define content safety guidelines, such as prohibited and sensitive topics, and disclaimer language, as well as brand safety guidelines to ensure the model’s outputs align with your brand’s voice, tone, values, and target audience.
System instructions have the following advantages over content filters:
You can define specific harms and topics you want to avoid, so you’re not restricted to a small set of categories.
You can be prescriptive and detailed. For example, instead of just saying “avoid nudity,” you can define what you mean by nudity in your cultural context and outline allowed exceptions.
You can iterate instructions to meet your needs. For example, if you notice that the instruction “avoid dangerous content” leads to the model being excessively cautious or avoiding a wider range of topics than intended, you can make the instruction more specific, such as “don’t generate violent content” or “avoid discussion of illegal drug use.”
However, system instructions have the following limitations:
They are theoretically more susceptible to zero-shot and other complex jailbreak techniques.
They can cause the model to be overly cautious on borderline topics.
In some situations, a complex system instruction for safety may inadvertently impact overall output quality.
We recommend using both content filters and system instructions.
Evaluate your safety configuration
You can create your own evaluation sets, and test model performance with your specific configurations ahead of time. We recommend creating separate harmful and benign sets, so you can measure how effective your configuration is at catching harmful content and how often it incorrectly blocks benign content.
Investing in an evaluation set can help reduce the time it takes to test the model when implementing changes in the future.
How to get started
Both content filters and system instructions play a role in ensuring safe and responsible use of Gemini. The best approach depends on your specific requirements and risk tolerance. To get started, check out content filters and system instructions for safety documentation.
AI Summary and Description: Yes
Summary: The text discusses risk mitigation strategies for organizations using generative AI-powered chatbots, emphasizing the integration of content filters and system instructions within Google Cloud’s Vertex AI. These tools aim to reduce harmful content generation while allowing for tailored content safety guidelines, offering a balanced approach to AI safety.
Detailed Description:
The passage highlights pivotal measures that organizations can adopt to minimize risks associated with generative AI technologies, particularly in preventing harmful content creation. With the rise of AI-driven chatbots, the text underscores the necessity of implementing defensive strategies to protect both users and the broader community from malicious exploitations of AI capabilities.
Key Points:
– **Context of Generative AI Risks**: As companies increasingly adopt generative AI solutions, the potential for threat actors to manipulate these systems to produce harmful content is a notable concern.
– **Content Filters**:
– Function as a **post-response defense mechanism**, analyzing AI-generated text to block replies based on predefined criteria.
– Operate independently of the underlying Gemini model, providing an additional security layer.
– Two categories of content filters:
– **Non-configurable safety filters**: Automatically block outputs that contain certain prohibited content including CSAM and PII.
– **Configurable content filters**: Allow organizations to set thresholds across categories such as hate speech and dangerous content, enabling customization based on specific organizational needs.
– **System Instructions**:
– Serve as a **proactive measure**, guiding the AI model in content generation and behavior settings.
– Allow businesses to craft clear guidelines for content safety, ensuring alignment with their values and target audience.
– Benefits include:
– Flexibility to define a broader range of banned topics beyond limited predefined categories.
– Ability to provide specific definitions for sensitive subjects, thus enhancing clarity and reducing ambiguity.
– Opportunity for iterative refinement of instructions to improve model outputs.
– **Challenges and Limitations**:
– Content filters may generate **false positives**, flagging benign content erroneously, which can disrupt user experience.
– System instructions might be vulnerable to advanced **jailbreak techniques**, potentially leading to undesired outputs.
– Over-caution in model responses may arise from general or overly broad system instructions.
– **Evaluating Safety**:
– Organizations can create bespoke evaluation sets to test the efficacy of their configurations, measuring both the ability to catch harmful content and the frequency of inadvertently blocking benign content.
– **Recommendation and Next Steps**:
– A dual approach utilizing both content filters and system instructions is suggested for optimal safety control.
– Organizations are encouraged to explore comprehensive documentation related to deploying these safety features in their implementations of Gemini.
In conclusion, the significance of these tools—content filters and system instructions—lies in their potential to enhance the safety of generative AI applications, reduce risks associated with harmful content, and promote responsible use within organizations. This guidance serves as a practical framework for security and compliance professionals tasked with safeguarding AI implementations in their environments.