In June 2023, Google launched its Secure AI Framework (SAIF), a comprehensive set of guidelines to address the rapidly evolving risks in artificial intelligence. Fast forward to October 2024, and SAIF has seen a significant upgrade, along with a free AI Risk Assessment Tool that helps organizations evaluate AI risk factors within their systems (available here). This update enhances SAIF’s role in guiding organizations through both high-level risks and the intricacies of AI and machine learning. Meanwhile, the OWASP Foundation's LLM Top 10 provides a targeted, tactical look at vulnerabilities in large language model (LLM) applications, offering practical steps to bolster security at the model level. On top of that, OWASP LLM Top 10 was the early mover in LLM risk mapping and as of now is more widely used in most industries. While significantly overlapping, these two frameworks cover a wide spectrum of AI security concerns – but how do they stack up, and is one better suited for certain scenarios? Let’s explore this in detail.
In June 2023, Google launched its Secure AI Framework (SAIF), a comprehensive set of guidelines to address the rapidly evolving risks in artificial intelligence. Fast forward to October 2024, and SAIF has seen a significant upgrade, along with a free AI Risk Assessment Tool that helps organizations evaluate AI risk factors within their systems (available here). This update enhances SAIF’s role in guiding organizations through both high-level risks and the intricacies of AI and machine learning. Meanwhile, the OWASP Foundation's LLM Top 10 provides a targeted, tactical look at vulnerabilities in large language model (LLM) applications, offering practical steps to bolster security at the model level. On top of that, OWASP LLM Top 10 was the early mover in LLM risk mapping and as of now is more widely used in most industries. While significantly overlapping, these two frameworks cover a wide spectrum of AI security concerns – but how do they stack up, and is one better suited for certain scenarios? Let’s explore this in detail.
In June 2023, Google launched its Secure AI Framework (SAIF), a comprehensive set of guidelines to address the rapidly evolving risks in artificial intelligence. Fast forward to October 2024, and SAIF has seen a significant upgrade, along with a free AI Risk Assessment Tool that helps organizations evaluate AI risk factors within their systems (available here). This update enhances SAIF’s role in guiding organizations through both high-level risks and the intricacies of AI and machine learning. Meanwhile, the OWASP Foundation's LLM Top 10 provides a targeted, tactical look at vulnerabilities in large language model (LLM) applications, offering practical steps to bolster security at the model level. On top of that, OWASP LLM Top 10 was the early mover in LLM risk mapping and as of now is more widely used in most industries. While significantly overlapping, these two frameworks cover a wide spectrum of AI security concerns – but how do they stack up, and is one better suited for certain scenarios? Let’s explore this in detail.
Google SAIF: Overview
Google's Secure AI Framework (SAIF) provides a comprehensive approach to integrating security and privacy measures into AI development. It categorizes the AI development process into four key areas: Data, Infrastructure, Model, and Application.
Main Components of AI Development in Google SAIF
Data
Encompasses data sources, filtering, and training datasets.
Ensures data integrity and quality for effective model training.
Key Risks: Data poisoning and unauthorized training data.
Infrastructure
Covers model frameworks, training workflows, storage, and deployment systems.
Focuses on secure environments for model development and serving.
Key Risks: Model source tampering, deployment tampering, and denial of service.
Model
Involves the trained model, input validation, and secure output handling.
Ensures the model is safe from exfiltration or exploitation.
Key Risks: Model exfiltration, insecure outputs, and evasion attacks.
Application
Includes AI-driven tools, user-facing applications, and plugins.
Addresses secure integration and proper permissions.
Key Risks: Prompt injection, insecure components, and rogue actions.
Key Risks and Mitigation Responsibilities
Google SAIF addresses 15 AI risks, divided into two parties who should be responsible for mitigating them:
Model Creators: Responsible for issues like data poisoning, model tampering, and denial of service.
Model Consumers: Focused on vulnerabilities such as prompt injection, insecure outputs, and rogue actions.
By addressing these components and risks, Google SAIF aims to guide organizations in developing and deploying AI systems securely and responsibly.
Google's Secure AI Framework (SAIF) provides a comprehensive approach to integrating security and privacy measures into AI development. It categorizes the AI development process into four key areas: Data, Infrastructure, Model, and Application.
Main Components of AI Development in Google SAIF
Data
Encompasses data sources, filtering, and training datasets.
Ensures data integrity and quality for effective model training.
Key Risks: Data poisoning and unauthorized training data.
Infrastructure
Covers model frameworks, training workflows, storage, and deployment systems.
Focuses on secure environments for model development and serving.
Key Risks: Model source tampering, deployment tampering, and denial of service.
Model
Involves the trained model, input validation, and secure output handling.
Ensures the model is safe from exfiltration or exploitation.
Key Risks: Model exfiltration, insecure outputs, and evasion attacks.
Application
Includes AI-driven tools, user-facing applications, and plugins.
Addresses secure integration and proper permissions.
Key Risks: Prompt injection, insecure components, and rogue actions.
Key Risks and Mitigation Responsibilities
Google SAIF addresses 15 AI risks, divided into two parties who should be responsible for mitigating them:
Model Creators: Responsible for issues like data poisoning, model tampering, and denial of service.
Model Consumers: Focused on vulnerabilities such as prompt injection, insecure outputs, and rogue actions.
By addressing these components and risks, Google SAIF aims to guide organizations in developing and deploying AI systems securely and responsibly.
Google's Secure AI Framework (SAIF) provides a comprehensive approach to integrating security and privacy measures into AI development. It categorizes the AI development process into four key areas: Data, Infrastructure, Model, and Application.
Main Components of AI Development in Google SAIF
Data
Encompasses data sources, filtering, and training datasets.
Ensures data integrity and quality for effective model training.
Key Risks: Data poisoning and unauthorized training data.
Infrastructure
Covers model frameworks, training workflows, storage, and deployment systems.
Focuses on secure environments for model development and serving.
Key Risks: Model source tampering, deployment tampering, and denial of service.
Model
Involves the trained model, input validation, and secure output handling.
Ensures the model is safe from exfiltration or exploitation.
Key Risks: Model exfiltration, insecure outputs, and evasion attacks.
Application
Includes AI-driven tools, user-facing applications, and plugins.
Addresses secure integration and proper permissions.
Key Risks: Prompt injection, insecure components, and rogue actions.
Key Risks and Mitigation Responsibilities
Google SAIF addresses 15 AI risks, divided into two parties who should be responsible for mitigating them:
Model Creators: Responsible for issues like data poisoning, model tampering, and denial of service.
Model Consumers: Focused on vulnerabilities such as prompt injection, insecure outputs, and rogue actions.
By addressing these components and risks, Google SAIF aims to guide organizations in developing and deploying AI systems securely and responsibly.
OWASP LLM Top 10: Overview
The OWASP LLM Top 10 Framework identifies the most critical security vulnerabilities in applications leveraging Large Language Models (LLMs). The framework aims to offer actionable guidance for developers, data scientists, and security professionals tasked with building or securing LLM-based systems. This framework addresses risks across the entire LLM lifecycle, emphasizing the importance of proactive security measures to stay ahead of these evolving threats.
Main Components of the LLM Ecosystem in OWASP's LLM Top 10
Application Services
User-facing systems that collect inputs and present outputs generated by LLMs.
Key Risks: Prompt injection and insecure output handling.
LLM Production Services
Core infrastructure managing LLM inference and fine-tuning.
Key Risks: Model theft, denial of service (DoS), and sensitive information disclosure.
Training Dataset & Processing
The data used for training and fine-tuning LLMs.
Key Risks: Training data poisoning and supply chain vulnerabilities.
Plugins & Extensions
Enhance LLM capabilities but can introduce security risks.
Key Risks: Insecure plugin design and excessive agency.
Downstream Services
External systems interfacing with LLMs for additional functionality.
Key Risks: Overreliance and excessive functionality.OWASP LLM Top 10 Risks
Key Risks and Mitigation Strategies
The OWASP LLM Top 10 Framework highlights critical vulnerabilities in large language model (LLM) applications, such as prompt injection, insecure output handling, training data poisoning, model denial of service (DoS), sensitive information disclosure, and model theft. Additional risks include insecure plugin design, excessive agency, overreliance on outputs, and supply chain vulnerabilities.
Mitigation strategies focus on input validation, a zero-trust approach to handling outputs, and securing training datasets through vetting and anomaly detection. Measures like rate limiting, monitoring, rigorous plugin testing, and access controls address specific vulnerabilities, while minimizing permissions and implementing robust authentication help prevent unauthorized access and model theft. These strategies provide practical tools for securing LLM-based systems effectively.
The OWASP LLM Top 10 Framework identifies the most critical security vulnerabilities in applications leveraging Large Language Models (LLMs). The framework aims to offer actionable guidance for developers, data scientists, and security professionals tasked with building or securing LLM-based systems. This framework addresses risks across the entire LLM lifecycle, emphasizing the importance of proactive security measures to stay ahead of these evolving threats.
Main Components of the LLM Ecosystem in OWASP's LLM Top 10
Application Services
User-facing systems that collect inputs and present outputs generated by LLMs.
Key Risks: Prompt injection and insecure output handling.
LLM Production Services
Core infrastructure managing LLM inference and fine-tuning.
Key Risks: Model theft, denial of service (DoS), and sensitive information disclosure.
Training Dataset & Processing
The data used for training and fine-tuning LLMs.
Key Risks: Training data poisoning and supply chain vulnerabilities.
Plugins & Extensions
Enhance LLM capabilities but can introduce security risks.
Key Risks: Insecure plugin design and excessive agency.
Downstream Services
External systems interfacing with LLMs for additional functionality.
Key Risks: Overreliance and excessive functionality.OWASP LLM Top 10 Risks
Key Risks and Mitigation Strategies
The OWASP LLM Top 10 Framework highlights critical vulnerabilities in large language model (LLM) applications, such as prompt injection, insecure output handling, training data poisoning, model denial of service (DoS), sensitive information disclosure, and model theft. Additional risks include insecure plugin design, excessive agency, overreliance on outputs, and supply chain vulnerabilities.
Mitigation strategies focus on input validation, a zero-trust approach to handling outputs, and securing training datasets through vetting and anomaly detection. Measures like rate limiting, monitoring, rigorous plugin testing, and access controls address specific vulnerabilities, while minimizing permissions and implementing robust authentication help prevent unauthorized access and model theft. These strategies provide practical tools for securing LLM-based systems effectively.
The OWASP LLM Top 10 Framework identifies the most critical security vulnerabilities in applications leveraging Large Language Models (LLMs). The framework aims to offer actionable guidance for developers, data scientists, and security professionals tasked with building or securing LLM-based systems. This framework addresses risks across the entire LLM lifecycle, emphasizing the importance of proactive security measures to stay ahead of these evolving threats.
Main Components of the LLM Ecosystem in OWASP's LLM Top 10
Application Services
User-facing systems that collect inputs and present outputs generated by LLMs.
Key Risks: Prompt injection and insecure output handling.
LLM Production Services
Core infrastructure managing LLM inference and fine-tuning.
Key Risks: Model theft, denial of service (DoS), and sensitive information disclosure.
Training Dataset & Processing
The data used for training and fine-tuning LLMs.
Key Risks: Training data poisoning and supply chain vulnerabilities.
Plugins & Extensions
Enhance LLM capabilities but can introduce security risks.
Key Risks: Insecure plugin design and excessive agency.
Downstream Services
External systems interfacing with LLMs for additional functionality.
Key Risks: Overreliance and excessive functionality.OWASP LLM Top 10 Risks
Key Risks and Mitigation Strategies
The OWASP LLM Top 10 Framework highlights critical vulnerabilities in large language model (LLM) applications, such as prompt injection, insecure output handling, training data poisoning, model denial of service (DoS), sensitive information disclosure, and model theft. Additional risks include insecure plugin design, excessive agency, overreliance on outputs, and supply chain vulnerabilities.
Mitigation strategies focus on input validation, a zero-trust approach to handling outputs, and securing training datasets through vetting and anomaly detection. Measures like rate limiting, monitoring, rigorous plugin testing, and access controls address specific vulnerabilities, while minimizing permissions and implementing robust authentication help prevent unauthorized access and model theft. These strategies provide practical tools for securing LLM-based systems effectively.
Comparing Google SAIF and OWASP LLM Top 10: A Security Risk Matrix
Google's SAIF and OWASP’s Top 10 for Large Language Model Applications are both valuable frameworks to support deployments of secure AI systems. However, they differ in scope and focus: SAIF offers a broad AI security framework with implications for data, models, and societal impacts, while OWASP dives deep into the technical vulnerabilities specific to LLM applications.
Here’s a comparison of the two frameworks, with an aligned view of their categories where possible:
1. Data Risks
| <b>Risk Area</b>| <b>Google SAIF</b>| <b>OWASP LLM Top 10</b>|
|------------------------|--------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|
| <b>Data Integrity</b> | <b>Data Poisoning:</b> Altering training data to degrade model performance or introduce backdoors. | <b>Training Data Poisoning:</b> Tampering with training data to compromise model behavior.|
| <b>Unauthorized Data Usage</b> | <b>Unauthorized Training Data: </b>Using data without proper authorization during model training. | Not explicitly covered.|
| <b>Data Handling</b> | <b>Excessive Data Handling:</b> Collecting or processing more data than necessary, leading to potential breaches. | <b>Supply Chain Vulnerabilities: </b>Compromised components or datasets undermining system integrity. |
| <b>Data Disclosure</b> | <b>Sensitive Data Disclosure:</b> Model inadvertently revealing sensitive information. | <b>Sensitive Information Disclosure:</b> LLM outputs revealing sensitive data.<br>Inferred Sensitive Data: Model inferring and disclosing sensitive information from inputs
2. Model Risks
| <b>Risk Area</b>| <b>Google SAIF</b>| <b>OWASP LLM Top 10</b>|
|------------------------|--------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|
| <b>Model Integrity</b> | <b>Model Source Tampering:</b> Compromising the model´s source code or parameters. | <b>Model Theft:</b> Unauthorized access and exfiltration of proprietary models.|
| <b>Model Exfiltration</b> | <b>Model Exfiltration: </b>Unauthorized extraction of the model, leading to intellectual property loss. | <b>Model Theft:</b> Unauthorized access and exfiltration of proprietary models.|
| <b>Reverse Engineering</b> | <b>Model Reverse Engineering:</b> Analyzing the model to extract proprietary information. | <b>Model Theft: </b>Unauthorized access and exfiltration of proprietary models.|
| <b>Model Evasion</b> | <b>Model Evasion:</b> Crafting inputs to bypass model detections or controls. | <b>Prompt Injection:</b> Manipulating LLMs via crafted inputs |
| <b>Output Security</b> | <b>Insecure Model Output:</b> Model generating outputs that could be exploited. | <b>Insecure Output Handling: </b>Neglecting to validate LLM outputs leading to security exploits. |
| <b>Autonomous Actions</b> | <b>Rogue Actions:</b> Model performing unintended actions autonomously. | <b>Excessive Agency:</b> Granting LLMs unchecked autonomy leading to unintended consequences
3. Deployment Risks
| <b>Risk Area</b>| <b>Google SAIF</b>| <b>OWASP LLM Top 10</b>|
|------------------------|--------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|
| <b>Component Security</b> | <b>Insecure Integrated Component:</b> Integrating components that introduce vulnerabilities. | <b>Insecure Plugin Design:</b> Plugins with insufficient access control leading to exploits.|
| <b>Service Availability</b> | <b>Denial of ML Service: </b>Overloading the model to disrupt its availability. | <b>Model Denial of Service:</b> Resource-heavy operations causing service disruptions.|
| <b>Deployment Security</b> | <b>Model Deployment Tampering:</b> Compromising the deployment environment to alter model behavior. | <b>Insecure Plugin Design: </b>Plugins with insufficient access control leading to exploits
4. Societal Risks
| <b>Risk Area</b>| <b>Google SAIF</b>| <b>OWASP LLM Top 10</b>|
|------------------------|--------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|
| <b>Overreliance on AI</b> | <b>Insecure Model Output:</b> Model output is not appropriately validated. | <b>Overreliance:</b> Failing to critically assess LLM outputs leading to compromised decision-making.|
| <b>Ethical Use of AI</b> | <b>Rogue Actions: </b>Model performing unintended actions autonomously with potential societal impact. | <b>Excessive Agency:</b> Granting LLMs unchecked autonomy leading to unintended consequences
Google's SAIF and OWASP’s Top 10 for Large Language Model Applications are both valuable frameworks to support deployments of secure AI systems. However, they differ in scope and focus: SAIF offers a broad AI security framework with implications for data, models, and societal impacts, while OWASP dives deep into the technical vulnerabilities specific to LLM applications.
Here’s a comparison of the two frameworks, with an aligned view of their categories where possible:
1. Data Risks
| <b>Risk Area</b>| <b>Google SAIF</b>| <b>OWASP LLM Top 10</b>|
|------------------------|--------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|
| <b>Data Integrity</b> | <b>Data Poisoning:</b> Altering training data to degrade model performance or introduce backdoors. | <b>Training Data Poisoning:</b> Tampering with training data to compromise model behavior.|
| <b>Unauthorized Data Usage</b> | <b>Unauthorized Training Data: </b>Using data without proper authorization during model training. | Not explicitly covered.|
| <b>Data Handling</b> | <b>Excessive Data Handling:</b> Collecting or processing more data than necessary, leading to potential breaches. | <b>Supply Chain Vulnerabilities: </b>Compromised components or datasets undermining system integrity. |
| <b>Data Disclosure</b> | <b>Sensitive Data Disclosure:</b> Model inadvertently revealing sensitive information. | <b>Sensitive Information Disclosure:</b> LLM outputs revealing sensitive data.<br>Inferred Sensitive Data: Model inferring and disclosing sensitive information from inputs
2. Model Risks
| <b>Risk Area</b>| <b>Google SAIF</b>| <b>OWASP LLM Top 10</b>|
|------------------------|--------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|
| <b>Model Integrity</b> | <b>Model Source Tampering:</b> Compromising the model´s source code or parameters. | <b>Model Theft:</b> Unauthorized access and exfiltration of proprietary models.|
| <b>Model Exfiltration</b> | <b>Model Exfiltration: </b>Unauthorized extraction of the model, leading to intellectual property loss. | <b>Model Theft:</b> Unauthorized access and exfiltration of proprietary models.|
| <b>Reverse Engineering</b> | <b>Model Reverse Engineering:</b> Analyzing the model to extract proprietary information. | <b>Model Theft: </b>Unauthorized access and exfiltration of proprietary models.|
| <b>Model Evasion</b> | <b>Model Evasion:</b> Crafting inputs to bypass model detections or controls. | <b>Prompt Injection:</b> Manipulating LLMs via crafted inputs |
| <b>Output Security</b> | <b>Insecure Model Output:</b> Model generating outputs that could be exploited. | <b>Insecure Output Handling: </b>Neglecting to validate LLM outputs leading to security exploits. |
| <b>Autonomous Actions</b> | <b>Rogue Actions:</b> Model performing unintended actions autonomously. | <b>Excessive Agency:</b> Granting LLMs unchecked autonomy leading to unintended consequences
3. Deployment Risks
| <b>Risk Area</b>| <b>Google SAIF</b>| <b>OWASP LLM Top 10</b>|
|------------------------|--------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|
| <b>Component Security</b> | <b>Insecure Integrated Component:</b> Integrating components that introduce vulnerabilities. | <b>Insecure Plugin Design:</b> Plugins with insufficient access control leading to exploits.|
| <b>Service Availability</b> | <b>Denial of ML Service: </b>Overloading the model to disrupt its availability. | <b>Model Denial of Service:</b> Resource-heavy operations causing service disruptions.|
| <b>Deployment Security</b> | <b>Model Deployment Tampering:</b> Compromising the deployment environment to alter model behavior. | <b>Insecure Plugin Design: </b>Plugins with insufficient access control leading to exploits
4. Societal Risks
| <b>Risk Area</b>| <b>Google SAIF</b>| <b>OWASP LLM Top 10</b>|
|------------------------|--------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|
| <b>Overreliance on AI</b> | <b>Insecure Model Output:</b> Model output is not appropriately validated. | <b>Overreliance:</b> Failing to critically assess LLM outputs leading to compromised decision-making.|
| <b>Ethical Use of AI</b> | <b>Rogue Actions: </b>Model performing unintended actions autonomously with potential societal impact. | <b>Excessive Agency:</b> Granting LLMs unchecked autonomy leading to unintended consequences
Google's SAIF and OWASP’s Top 10 for Large Language Model Applications are both valuable frameworks to support deployments of secure AI systems. However, they differ in scope and focus: SAIF offers a broad AI security framework with implications for data, models, and societal impacts, while OWASP dives deep into the technical vulnerabilities specific to LLM applications.
Here’s a comparison of the two frameworks, with an aligned view of their categories where possible:
1. Data Risks
| <b>Risk Area</b>| <b>Google SAIF</b>| <b>OWASP LLM Top 10</b>|
|------------------------|--------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|
| <b>Data Integrity</b> | <b>Data Poisoning:</b> Altering training data to degrade model performance or introduce backdoors. | <b>Training Data Poisoning:</b> Tampering with training data to compromise model behavior.|
| <b>Unauthorized Data Usage</b> | <b>Unauthorized Training Data: </b>Using data without proper authorization during model training. | Not explicitly covered.|
| <b>Data Handling</b> | <b>Excessive Data Handling:</b> Collecting or processing more data than necessary, leading to potential breaches. | <b>Supply Chain Vulnerabilities: </b>Compromised components or datasets undermining system integrity. |
| <b>Data Disclosure</b> | <b>Sensitive Data Disclosure:</b> Model inadvertently revealing sensitive information. | <b>Sensitive Information Disclosure:</b> LLM outputs revealing sensitive data.<br>Inferred Sensitive Data: Model inferring and disclosing sensitive information from inputs
2. Model Risks
| <b>Risk Area</b>| <b>Google SAIF</b>| <b>OWASP LLM Top 10</b>|
|------------------------|--------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|
| <b>Model Integrity</b> | <b>Model Source Tampering:</b> Compromising the model´s source code or parameters. | <b>Model Theft:</b> Unauthorized access and exfiltration of proprietary models.|
| <b>Model Exfiltration</b> | <b>Model Exfiltration: </b>Unauthorized extraction of the model, leading to intellectual property loss. | <b>Model Theft:</b> Unauthorized access and exfiltration of proprietary models.|
| <b>Reverse Engineering</b> | <b>Model Reverse Engineering:</b> Analyzing the model to extract proprietary information. | <b>Model Theft: </b>Unauthorized access and exfiltration of proprietary models.|
| <b>Model Evasion</b> | <b>Model Evasion:</b> Crafting inputs to bypass model detections or controls. | <b>Prompt Injection:</b> Manipulating LLMs via crafted inputs |
| <b>Output Security</b> | <b>Insecure Model Output:</b> Model generating outputs that could be exploited. | <b>Insecure Output Handling: </b>Neglecting to validate LLM outputs leading to security exploits. |
| <b>Autonomous Actions</b> | <b>Rogue Actions:</b> Model performing unintended actions autonomously. | <b>Excessive Agency:</b> Granting LLMs unchecked autonomy leading to unintended consequences
3. Deployment Risks
| <b>Risk Area</b>| <b>Google SAIF</b>| <b>OWASP LLM Top 10</b>|
|------------------------|--------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|
| <b>Component Security</b> | <b>Insecure Integrated Component:</b> Integrating components that introduce vulnerabilities. | <b>Insecure Plugin Design:</b> Plugins with insufficient access control leading to exploits.|
| <b>Service Availability</b> | <b>Denial of ML Service: </b>Overloading the model to disrupt its availability. | <b>Model Denial of Service:</b> Resource-heavy operations causing service disruptions.|
| <b>Deployment Security</b> | <b>Model Deployment Tampering:</b> Compromising the deployment environment to alter model behavior. | <b>Insecure Plugin Design: </b>Plugins with insufficient access control leading to exploits
4. Societal Risks
| <b>Risk Area</b>| <b>Google SAIF</b>| <b>OWASP LLM Top 10</b>|
|------------------------|--------------------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------|
| <b>Overreliance on AI</b> | <b>Insecure Model Output:</b> Model output is not appropriately validated. | <b>Overreliance:</b> Failing to critically assess LLM outputs leading to compromised decision-making.|
| <b>Ethical Use of AI</b> | <b>Rogue Actions: </b>Model performing unintended actions autonomously with potential societal impact. | <b>Excessive Agency:</b> Granting LLMs unchecked autonomy leading to unintended consequences
Conclusion: How to choose the right framework for your GenAI security needs
Google’s SAIF and OWASP’s LLM Top 10 offer complementary frameworks for securing AI systems, but their distinct focuses cater to different organizational needs.
Google SAIF provides a broad strategic framework, emphasizing high-level risk management across data integrity, deployment security, and societal impact. It is ideal for organizations seeking to align AI security with overarching business strategies and compliance with regulatory requirements.
OWASP’s LLM Top 10 delivers tactical precision, targeting specific vulnerabilities in large language models such as prompt injection, insecure plugins, and excessive autonomy. Its actionable guidelines are highly relevant for development teams tasked with securing LLM-heavy applications against immediate, targeted threats.
For enterprises, the choice depends on security goals and resources:
Strategic Needs: If aligning AI security with business goals or regulatory compliance is the priority, SAIF provides the necessary high-level perspective.
Focused Protection: If addressing specific vulnerabilities in LLM-based systems is critical, OWASP’s detailed insights are invaluable.
Blended approach for comprehensive AI security
As AI continues to evolve, combining the strengths of both frameworks can offer the most robust security:
Google's SAIF ensures a comprehensive, strategic view of AI risks, setting a foundation for enterprise-wide governance.
OWASP’s LLM Top 10 enables precision in addressing vulnerabilities unique to LLMs, complementing SAIF’s broader approach.
Adopting the best of both frameworks allows enterprises to establish a balanced, layered security posture that meets both high-level objectives and specific technical needs, ensuring resilience against an ever-expanding array of AI threats.
Check out Google's SAIF Risk Assessment Tool for a hands-on assessment to begin strengthening your organization’s AI security posture.
Google’s SAIF and OWASP’s LLM Top 10 offer complementary frameworks for securing AI systems, but their distinct focuses cater to different organizational needs.
Google SAIF provides a broad strategic framework, emphasizing high-level risk management across data integrity, deployment security, and societal impact. It is ideal for organizations seeking to align AI security with overarching business strategies and compliance with regulatory requirements.
OWASP’s LLM Top 10 delivers tactical precision, targeting specific vulnerabilities in large language models such as prompt injection, insecure plugins, and excessive autonomy. Its actionable guidelines are highly relevant for development teams tasked with securing LLM-heavy applications against immediate, targeted threats.
For enterprises, the choice depends on security goals and resources:
Strategic Needs: If aligning AI security with business goals or regulatory compliance is the priority, SAIF provides the necessary high-level perspective.
Focused Protection: If addressing specific vulnerabilities in LLM-based systems is critical, OWASP’s detailed insights are invaluable.
Blended approach for comprehensive AI security
As AI continues to evolve, combining the strengths of both frameworks can offer the most robust security:
Google's SAIF ensures a comprehensive, strategic view of AI risks, setting a foundation for enterprise-wide governance.
OWASP’s LLM Top 10 enables precision in addressing vulnerabilities unique to LLMs, complementing SAIF’s broader approach.
Adopting the best of both frameworks allows enterprises to establish a balanced, layered security posture that meets both high-level objectives and specific technical needs, ensuring resilience against an ever-expanding array of AI threats.
Check out Google's SAIF Risk Assessment Tool for a hands-on assessment to begin strengthening your organization’s AI security posture.
Google’s SAIF and OWASP’s LLM Top 10 offer complementary frameworks for securing AI systems, but their distinct focuses cater to different organizational needs.
Google SAIF provides a broad strategic framework, emphasizing high-level risk management across data integrity, deployment security, and societal impact. It is ideal for organizations seeking to align AI security with overarching business strategies and compliance with regulatory requirements.
OWASP’s LLM Top 10 delivers tactical precision, targeting specific vulnerabilities in large language models such as prompt injection, insecure plugins, and excessive autonomy. Its actionable guidelines are highly relevant for development teams tasked with securing LLM-heavy applications against immediate, targeted threats.
For enterprises, the choice depends on security goals and resources:
Strategic Needs: If aligning AI security with business goals or regulatory compliance is the priority, SAIF provides the necessary high-level perspective.
Focused Protection: If addressing specific vulnerabilities in LLM-based systems is critical, OWASP’s detailed insights are invaluable.
Blended approach for comprehensive AI security
As AI continues to evolve, combining the strengths of both frameworks can offer the most robust security:
Google's SAIF ensures a comprehensive, strategic view of AI risks, setting a foundation for enterprise-wide governance.
OWASP’s LLM Top 10 enables precision in addressing vulnerabilities unique to LLMs, complementing SAIF’s broader approach.
Adopting the best of both frameworks allows enterprises to establish a balanced, layered security posture that meets both high-level objectives and specific technical needs, ensuring resilience against an ever-expanding array of AI threats.
Check out Google's SAIF Risk Assessment Tool for a hands-on assessment to begin strengthening your organization’s AI security posture.
Deploy your AI apps with confidence
Deploy your AI apps with confidence
Deploy your AI apps with confidence
