The Complete Platform for GenAI Security

Equally as important as inspecting user prompts before they get to an organization’s systems, is ensuring that responses by LLMs are safe and do not contain toxic or harmful content that could be damaging to an organization.

Inappropriate or off-brand content generated by GenAI applications can result in public relations challenges and harm the company's image, hence moderating content produced by LLMs - given their non-deterministic nature - is crucial.

Key Concerns:

1. Toxic or damaging content: Ensuring your GenAI apps don't expose toxic, biased, racist or offensive material to your stakeholders.
2. Competitive disadvantage: Preventing your GenAI apps from inadvertently promoting or supporting competitors.
3. Off-brand behavior: Guaranteeing your GenAI apps adhere to the desired behavior and tone of your brand.

Data privacy has become increasingly crucial in the era of GenAI tool proliferation. With the rise in GenAI tool usage, the likelihood of sharing confidential data has escalated.

LLM applications have the potential to reveal sensitive information, proprietary algorithms, or other confidential details through their output. This can result in unauthorized access to sensitive data, intellectual property, privacy violations, and other security breaches. It is important for consumers of LLM applications to be aware of how to safely interact with LLMs and identify the risks associated with unintentionally inputting sensitive data that may be subsequently returned by the LLM in output elsewhere.

Key Concerns:

1. Employees sharing confidential information through GenAI tools
2. Developers exfiltrating secrets through AI code assistants
3. Homegrown GenAI apps leaking exposing company information

Denial of Wallet Attacks, alongside Denial of Service, are critical security concerns where an attacker excessively engages with a Large Language Model (LLM) application, leading to substantial resource consumption. This not only degrades the quality of service for legitimate users but also can result in significant financial costs due to overuse of resources. Attackers can exploit this by using a jailbroken interface to covertly access third-party LLMs like OpenAI's GPT, essentially utilizing your application as a free proxy to OpenAI.

Key Concerns:‍

1. Application Downtime: Risk of service unavailability due to resource overuse.
2. Performance Degradation: Slower response times and reduced efficiency.
3. Financial Implications: Potential for incurring high operational costs.

Learn more about Denial of Wallet attacks: https://www.prompt.security/blog/denial-of-wallet-on-genai-apps-ddow

Indirect Prompt Injection occurs when an LLM processes input from external sources that are under the control of an attacker, such as certain websites or tools. In such cases, the attacker can embed a hidden prompt in the external content, effectively hijacking the conversation's context. This results in the destabilization of the LLM's output, potentially allowing the attacker to manipulate the user or interact with other systems accessible by the LLM. Notably, these indirect prompt injections do not need to be visible or readable by humans, as long as they can be parsed by the LLM. A typical example is a ChatGPT web plugin that could unknowingly process a malicious prompt from an attacker's website, often designed to be inconspicuous to human observers (white font, for instance).

Key Concerns:

1. Unauthorized data exfiltration: Extracting sensitive data without permission.
2. Remote code execution: Running malicious code through the LLM.
3. DDoS (Distributed Denial of Service): Overloading the system to disrupt services.
4. Social engineering: Manipulating the LLM to behave differently than planned.

LLM plugins are extensions that, when enabled, are called automatically by the model during user interactions. They are driven by the model, and there is no application control over the execution. Furthermore, to deal with context-size limitations, plugins are likely to implement free-text inputs from the model with no validation or type checking. This allows a potential attacker to construct a malicious request to the plugin, which could result in a wide range of undesired behaviors, up to and including remote code execution.

‍

Key Concerns:

1. Malicious Code Execution: Preventing unauthorized execution of harmful code.
2. SQL Injection: Protecting against unauthorized database access or manipulation.
3. Cross-Site Scripting (XSS) and Cross-Site Request Forgery (CSRF): Defending against web-based attacks that can compromise user data and interactions.

Jailbreaking, a type of Prompt Injection refers to the engineering of prompts to exploit model biases and generate outputs that may not align with their intended behavior, original purpose or established guidelines.

By carefully crafting inputs that exploit system vulnerabilities, the LLM can eventually respond without its usual restrictions or moderation. There have been some notable examples, such as the "DAN" or "multi-shot jailbreaking", where the AI systems responded without their usual constraints.

Key Concerns:

1. Brand Reputation: Preventing damage to the organization's public image due to undesired AI behavior.
2. Decreased Performance: Ensuring the GenAI application functions as designed, without unexpected deviations.
3. Unsafe Customer Experience: Protecting users from potentially harmful or inappropriate interactions with the AI system.

The emergence of GenAI technologies is raising substantial legal concerns within organizations. These concerns stem primarily from the lack of oversight and auditing of GenAI tools and their outputs, as well as the potential mishandling of intellectual property. In particular, these issues can manifest as unauthorized use or "Shadow AI," unintentional disclosure of sensitive intellectual property to the tools, migration of intellectual property through these tools, and the generation of harmful or offensive content that may reach customers.

Key Concerns:

1. Absence of Audit and Visibility: Addressing the challenge of unmonitored GenAI usage or "Shadow AI."
2. Intellectual Property Disclosure: Preventing sharing of proprietary information with GenAI tools.
3. Intellectual Property Migration: Safeguarding against the unintentional transfer of intellectual assets through GenAI tools to your company.
4. Generation of Harmful or Offensive Content: Ensuring GenAI tools do not produce content that could harm customers or the company's reputation.

As the integration of Large Language Models (LLMs) with various tools like databases, APIs, and code interpreters increases, so does the risk of privilege escalation. This GenAI risk involves the potential misuse of LLM privileges to gain unauthorized access and control within an organization’s digital environment.

Key Concerns:

1. Privilege Escalation: Unauthorized elevation of access rights.
2. Unauthorized Data Access: Accessing sensitive data without proper authorization.
3. System Compromise: Gaining control over systems beyond intended limits.
4. Denial of Service: Disrupting services by overloading or manipulating systems.

Prompt Injection is a cybersecurity threat where attackers manipulate a large language model (LLM) through carefully crafted inputs. This manipulation, often referred to as "jailbreaking" tricks the LLM into executing the attacker's intentions. This threat becomes particularly concerning when the LLM is integrated with other tools such as internal databases, APIs, or code interpreters, creating a new attack surface.

Key Concerns:

1. Unauthorized data exfiltration: Extracting sensitive data without permission.
2. Remote code execution: Running malicious code through the LLM.
3. DDoS (Distributed Denial of Service): Overloading the system to disrupt services.
4. Social engineering: Manipulating the LLM to behave differently than its intended use.

Learn more about Prompt Injection: https://www.prompt.security/blog/prompt-injection-101

Prompt Leak is a specific form of prompt injection where a Large Language Model (LLM) inadvertently reveals its system instructions or internal logic. This issue arises when prompts are engineered to extract the underlying system prompt of a GenAI application. As prompt engineering becomes increasingly integral to the development of GenAI apps, any unintentional disclosure of these prompts can be considered as exposure of proprietary code or intellectual property.

Key Concerns:

1. Intellectual Property Disclosure: Preventing the unauthorized revelation of proprietary information embedded in system prompts.
2. Recon for Downstream Attacks: Avoiding the leak of system prompts which could serve as reconnaissance for more damaging prompt injections.
3. Brand Reputation Damage: Protecting the organization's public image from the fallout of accidental prompt disclosure which might contain embarrassing information.

ChatGPT marked the beginning of the widespread adoption of GenAI tools. Today, in the average company, we observe employees using over 30 different AI tools as part of their jobs, most of them without visibility to the IT Security team. Mastering and managing these tools is crucial for success.

Key Concerns:

1. Limited Visibility: Understanding the full scope of GenAI tool usage within the company.
2. Absence of Governance: Establishing effective control over the usage of GenAI tools.
3. Compliance Risks: Mitigating the risk of violating regulatory standards.
4. Sensitive Data Exposure: Preventing employees from exposing sensitive or confidential information to GenAI tools and copilots.

A jailbroken Large Language Model (LLM) behaving unpredictably can pose significant risks, potentially endangering an organization, its employees, or customers. The repercussions range from embarrassing social media posts to negative customer experiences, and may even include legal complications. To safeguard against such issues, it’s crucial to implement protective measures.

Key Concerns:

1. Toxicity: Preventing harmful or offensive content.
2. Bias: Ensuring fair and impartial interactions.
3. Racism: Avoiding racially insensitive or discriminatory content.
4. Brand Reputation: Maintaining a positive public image.
5. Inappropriate Sexual Content: Filtering out unsuitable sexual material.

As the integration of Large Language Models (LLMs) with various tools like databases, APIs, and code interpreters increases, so does the risk of privilege escalation. This GenAI risk involves the potential misuse of LLM privileges to gain unauthorized access and control within an organization’s digital environment.

Key Concerns:

1. Privilege Escalation: Unauthorized elevation of access rights.
2. Unauthorized Data Access: Accessing sensitive data without proper authorization.
3. System Compromise: Gaining control over systems beyond intended limits.
4. Denial of Service: Disrupting services by overloading or manipulating systems.

LLM plugins are extensions that, when enabled, are called automatically by the model during user interactions. They are driven by the model, and there is no application control over the execution. Furthermore, to deal with context-size limitations, plugins are likely to implement free-text inputs from the model with no validation or type checking. This allows a potential attacker to construct a malicious request to the plugin, which could result in a wide range of undesired behaviors, up to and including remote code execution.

‍

Key Concerns:

1. Malicious Code Execution: Preventing unauthorized execution of harmful code.
2. SQL Injection: Protecting against unauthorized database access or manipulation.
3. Cross-Site Scripting (XSS) and Cross-Site Request Forgery (CSRF): Defending against web-based attacks that can compromise user data and interactions.

Equally as important as inspecting user prompts before they get to an organization’s systems, is ensuring that responses by LLMs are safe and do not contain toxic or harmful content that could be damaging to an organization.

Inappropriate or off-brand content generated by GenAI applications can result in public relations challenges and harm the company's image, hence moderating content produced by LLMs - given their non-deterministic nature - is crucial.

Key Concerns:

1. Toxic or damaging content: Ensuring your GenAI apps don't expose toxic, biased, racist or offensive material to your stakeholders.
2. Competitive disadvantage: Preventing your GenAI apps from inadvertently promoting or supporting competitors.
3. Off-brand behavior: Guaranteeing your GenAI apps adhere to the desired behavior and tone of your brand.

ChatGPT marked the beginning of the widespread adoption of GenAI tools. Today, in the average company, we observe employees using over 30 different AI tools as part of their jobs, most of them without visibility to the IT Security team. Mastering and managing these tools is crucial for success.

Key Concerns:

1. Limited Visibility: Understanding the full scope of GenAI tool usage within the company.
2. Absence of Governance: Establishing effective control over the usage of GenAI tools.
3. Compliance Risks: Mitigating the risk of violating regulatory standards.
4. Sensitive Data Exposure: Preventing employees from exposing sensitive or confidential information to GenAI tools and copilots.

Data privacy has become increasingly crucial in the era of GenAI tool proliferation. With the rise in GenAI tool usage, the likelihood of sharing confidential data has escalated.

LLM applications have the potential to reveal sensitive information, proprietary algorithms, or other confidential details through their output. This can result in unauthorized access to sensitive data, intellectual property, privacy violations, and other security breaches. It is important for consumers of LLM applications to be aware of how to safely interact with LLMs and identify the risks associated with unintentionally inputting sensitive data that may be subsequently returned by the LLM in output elsewhere.

Key Concerns:

1. Employees sharing confidential information through GenAI tools
2. Developers exfiltrating secrets through AI code assistants
3. Homegrown GenAI apps leaking exposing company information

Prompt Injection is a cybersecurity threat where attackers manipulate a large language model (LLM) through carefully crafted inputs. This manipulation, often referred to as "jailbreaking" tricks the LLM into executing the attacker's intentions. This threat becomes particularly concerning when the LLM is integrated with other tools such as internal databases, APIs, or code interpreters, creating a new attack surface.

Key Concerns:

1. Unauthorized data exfiltration: Extracting sensitive data without permission.
2. Remote code execution: Running malicious code through the LLM.
3. DDoS (Distributed Denial of Service): Overloading the system to disrupt services.
4. Social engineering: Manipulating the LLM to behave differently than its intended use.

Learn more about Prompt Injection: https://www.prompt.security/blog/prompt-injection-101

Prompt Leak is a specific form of prompt injection where a Large Language Model (LLM) inadvertently reveals its system instructions or internal logic. This issue arises when prompts are engineered to extract the underlying system prompt of a GenAI application. As prompt engineering becomes increasingly integral to the development of GenAI apps, any unintentional disclosure of these prompts can be considered as exposure of proprietary code or intellectual property.

Key Concerns:

1. Intellectual Property Disclosure: Preventing the unauthorized revelation of proprietary information embedded in system prompts.
2. Recon for Downstream Attacks: Avoiding the leak of system prompts which could serve as reconnaissance for more damaging prompt injections.
3. Brand Reputation Damage: Protecting the organization's public image from the fallout of accidental prompt disclosure which might contain embarrassing information.

Denial of Wallet Attacks, alongside Denial of Service, are critical security concerns where an attacker excessively engages with a Large Language Model (LLM) application, leading to substantial resource consumption. This not only degrades the quality of service for legitimate users but also can result in significant financial costs due to overuse of resources. Attackers can exploit this by using a jailbroken interface to covertly access third-party LLMs like OpenAI's GPT, essentially utilizing your application as a free proxy to OpenAI.

Key Concerns:‍

1. Application Downtime: Risk of service unavailability due to resource overuse.
2. Performance Degradation: Slower response times and reduced efficiency.
3. Financial Implications: Potential for incurring high operational costs.

Learn more about Denial of Wallet attacks: https://www.prompt.security/blog/denial-of-wallet-on-genai-apps-ddow

The emergence of GenAI technologies is raising substantial legal concerns within organizations. These concerns stem primarily from the lack of oversight and auditing of GenAI tools and their outputs, as well as the potential mishandling of intellectual property. In particular, these issues can manifest as unauthorized use or "Shadow AI," unintentional disclosure of sensitive intellectual property to the tools, migration of intellectual property through these tools, and the generation of harmful or offensive content that may reach customers.

Key Concerns:

1. Absence of Audit and Visibility: Addressing the challenge of unmonitored GenAI usage or "Shadow AI."
2. Intellectual Property Disclosure: Preventing sharing of proprietary information with GenAI tools.
3. Intellectual Property Migration: Safeguarding against the unintentional transfer of intellectual assets through GenAI tools to your company.
4. Generation of Harmful or Offensive Content: Ensuring GenAI tools do not produce content that could harm customers or the company's reputation.

A jailbroken Large Language Model (LLM) behaving unpredictably can pose significant risks, potentially endangering an organization, its employees, or customers. The repercussions range from embarrassing social media posts to negative customer experiences, and may even include legal complications. To safeguard against such issues, it’s crucial to implement protective measures.

Key Concerns:

1. Toxicity: Preventing harmful or offensive content.
2. Bias: Ensuring fair and impartial interactions.
3. Racism: Avoiding racially insensitive or discriminatory content.
4. Brand Reputation: Maintaining a positive public image.
5. Inappropriate Sexual Content: Filtering out unsuitable sexual material.

Jailbreaking, a type of Prompt Injection refers to the engineering of prompts to exploit model biases and generate outputs that may not align with their intended behavior, original purpose or established guidelines.

By carefully crafting inputs that exploit system vulnerabilities, the LLM can eventually respond without its usual restrictions or moderation. There have been some notable examples, such as the "DAN" or "multi-shot jailbreaking", where the AI systems responded without their usual constraints.

Key Concerns:

1. Brand Reputation: Preventing damage to the organization's public image due to undesired AI behavior.
2. Decreased Performance: Ensuring the GenAI application functions as designed, without unexpected deviations.
3. Unsafe Customer Experience: Protecting users from potentially harmful or inappropriate interactions with the AI system.

Indirect Prompt Injection occurs when an LLM processes input from external sources that are under the control of an attacker, such as certain websites or tools. In such cases, the attacker can embed a hidden prompt in the external content, effectively hijacking the conversation's context. This results in the destabilization of the LLM's output, potentially allowing the attacker to manipulate the user or interact with other systems accessible by the LLM. Notably, these indirect prompt injections do not need to be visible or readable by humans, as long as they can be parsed by the LLM. A typical example is a ChatGPT web plugin that could unknowingly process a malicious prompt from an attacker's website, often designed to be inconspicuous to human observers (white font, for instance).

Key Concerns:

1. Unauthorized data exfiltration: Extracting sensitive data without permission.
2. Remote code execution: Running malicious code through the LLM.
3. DDoS (Distributed Denial of Service): Overloading the system to disrupt services.
4. Social engineering: Manipulating the LLM to behave differently than planned.

As the integration of Large Language Models (LLMs) with various tools like databases, APIs, and code interpreters increases, so does the risk of privilege escalation. This GenAI risk involves the potential misuse of LLM privileges to gain unauthorized access and control within an organization’s digital environment.

Key Concerns:

1. Privilege Escalation: Unauthorized elevation of access rights.
2. Unauthorized Data Access: Accessing sensitive data without proper authorization.
3. System Compromise: Gaining control over systems beyond intended limits.
4. Denial of Service: Disrupting services by overloading or manipulating systems.

LLM plugins are extensions that, when enabled, are called automatically by the model during user interactions. They are driven by the model, and there is no application control over the execution. Furthermore, to deal with context-size limitations, plugins are likely to implement free-text inputs from the model with no validation or type checking. This allows a potential attacker to construct a malicious request to the plugin, which could result in a wide range of undesired behaviors, up to and including remote code execution.

‍

Key Concerns:

1. Malicious Code Execution: Preventing unauthorized execution of harmful code.
2. SQL Injection: Protecting against unauthorized database access or manipulation.
3. Cross-Site Scripting (XSS) and Cross-Site Request Forgery (CSRF): Defending against web-based attacks that can compromise user data and interactions.

Equally as important as inspecting user prompts before they get to an organization’s systems, is ensuring that responses by LLMs are safe and do not contain toxic or harmful content that could be damaging to an organization.

Inappropriate or off-brand content generated by GenAI applications can result in public relations challenges and harm the company's image, hence moderating content produced by LLMs - given their non-deterministic nature - is crucial.

Key Concerns:

1. Toxic or damaging content: Ensuring your GenAI apps don't expose toxic, biased, racist or offensive material to your stakeholders.
2. Competitive disadvantage: Preventing your GenAI apps from inadvertently promoting or supporting competitors.
3. Off-brand behavior: Guaranteeing your GenAI apps adhere to the desired behavior and tone of your brand.

ChatGPT marked the beginning of the widespread adoption of GenAI tools. Today, in the average company, we observe employees using over 30 different AI tools as part of their jobs, most of them without visibility to the IT Security team. Mastering and managing these tools is crucial for success.

Key Concerns:

1. Limited Visibility: Understanding the full scope of GenAI tool usage within the company.
2. Absence of Governance: Establishing effective control over the usage of GenAI tools.
3. Compliance Risks: Mitigating the risk of violating regulatory standards.
4. Sensitive Data Exposure: Preventing employees from exposing sensitive or confidential information to GenAI tools and copilots.

Data privacy has become increasingly crucial in the era of GenAI tool proliferation. With the rise in GenAI tool usage, the likelihood of sharing confidential data has escalated.

LLM applications have the potential to reveal sensitive information, proprietary algorithms, or other confidential details through their output. This can result in unauthorized access to sensitive data, intellectual property, privacy violations, and other security breaches. It is important for consumers of LLM applications to be aware of how to safely interact with LLMs and identify the risks associated with unintentionally inputting sensitive data that may be subsequently returned by the LLM in output elsewhere.

Key Concerns:

1. Employees sharing confidential information through GenAI tools
2. Developers exfiltrating secrets through AI code assistants
3. Homegrown GenAI apps leaking exposing company information

Prompt Injection is a cybersecurity threat where attackers manipulate a large language model (LLM) through carefully crafted inputs. This manipulation, often referred to as "jailbreaking" tricks the LLM into executing the attacker's intentions. This threat becomes particularly concerning when the LLM is integrated with other tools such as internal databases, APIs, or code interpreters, creating a new attack surface.

Key Concerns:

1. Unauthorized data exfiltration: Extracting sensitive data without permission.
2. Remote code execution: Running malicious code through the LLM.
3. DDoS (Distributed Denial of Service): Overloading the system to disrupt services.
4. Social engineering: Manipulating the LLM to behave differently than its intended use.

Learn more about Prompt Injection: https://www.prompt.security/blog/prompt-injection-101

Prompt Leak is a specific form of prompt injection where a Large Language Model (LLM) inadvertently reveals its system instructions or internal logic. This issue arises when prompts are engineered to extract the underlying system prompt of a GenAI application. As prompt engineering becomes increasingly integral to the development of GenAI apps, any unintentional disclosure of these prompts can be considered as exposure of proprietary code or intellectual property.

Key Concerns:

1. Intellectual Property Disclosure: Preventing the unauthorized revelation of proprietary information embedded in system prompts.
2. Recon for Downstream Attacks: Avoiding the leak of system prompts which could serve as reconnaissance for more damaging prompt injections.
3. Brand Reputation Damage: Protecting the organization's public image from the fallout of accidental prompt disclosure which might contain embarrassing information.

Denial of Wallet Attacks, alongside Denial of Service, are critical security concerns where an attacker excessively engages with a Large Language Model (LLM) application, leading to substantial resource consumption. This not only degrades the quality of service for legitimate users but also can result in significant financial costs due to overuse of resources. Attackers can exploit this by using a jailbroken interface to covertly access third-party LLMs like OpenAI's GPT, essentially utilizing your application as a free proxy to OpenAI.

Key Concerns:‍

1. Application Downtime: Risk of service unavailability due to resource overuse.
2. Performance Degradation: Slower response times and reduced efficiency.
3. Financial Implications: Potential for incurring high operational costs.

Learn more about Denial of Wallet attacks: https://www.prompt.security/blog/denial-of-wallet-on-genai-apps-ddow

The emergence of GenAI technologies is raising substantial legal concerns within organizations. These concerns stem primarily from the lack of oversight and auditing of GenAI tools and their outputs, as well as the potential mishandling of intellectual property. In particular, these issues can manifest as unauthorized use or "Shadow AI," unintentional disclosure of sensitive intellectual property to the tools, migration of intellectual property through these tools, and the generation of harmful or offensive content that may reach customers.

Key Concerns:

1. Absence of Audit and Visibility: Addressing the challenge of unmonitored GenAI usage or "Shadow AI."
2. Intellectual Property Disclosure: Preventing sharing of proprietary information with GenAI tools.
3. Intellectual Property Migration: Safeguarding against the unintentional transfer of intellectual assets through GenAI tools to your company.
4. Generation of Harmful or Offensive Content: Ensuring GenAI tools do not produce content that could harm customers or the company's reputation.

A jailbroken Large Language Model (LLM) behaving unpredictably can pose significant risks, potentially endangering an organization, its employees, or customers. The repercussions range from embarrassing social media posts to negative customer experiences, and may even include legal complications. To safeguard against such issues, it’s crucial to implement protective measures.

Key Concerns:

1. Toxicity: Preventing harmful or offensive content.
2. Bias: Ensuring fair and impartial interactions.
3. Racism: Avoiding racially insensitive or discriminatory content.
4. Brand Reputation: Maintaining a positive public image.
5. Inappropriate Sexual Content: Filtering out unsuitable sexual material.

Jailbreaking, a type of Prompt Injection refers to the engineering of prompts to exploit model biases and generate outputs that may not align with their intended behavior, original purpose or established guidelines.

By carefully crafting inputs that exploit system vulnerabilities, the LLM can eventually respond without its usual restrictions or moderation. There have been some notable examples, such as the "DAN" or "multi-shot jailbreaking", where the AI systems responded without their usual constraints.

Key Concerns:

1. Brand Reputation: Preventing damage to the organization's public image due to undesired AI behavior.
2. Decreased Performance: Ensuring the GenAI application functions as designed, without unexpected deviations.
3. Unsafe Customer Experience: Protecting users from potentially harmful or inappropriate interactions with the AI system.

Indirect Prompt Injection occurs when an LLM processes input from external sources that are under the control of an attacker, such as certain websites or tools. In such cases, the attacker can embed a hidden prompt in the external content, effectively hijacking the conversation's context. This results in the destabilization of the LLM's output, potentially allowing the attacker to manipulate the user or interact with other systems accessible by the LLM. Notably, these indirect prompt injections do not need to be visible or readable by humans, as long as they can be parsed by the LLM. A typical example is a ChatGPT web plugin that could unknowingly process a malicious prompt from an attacker's website, often designed to be inconspicuous to human observers (white font, for instance).

Key Concerns:

1. Unauthorized data exfiltration: Extracting sensitive data without permission.
2. Remote code execution: Running malicious code through the LLM.
3. DDoS (Distributed Denial of Service): Overloading the system to disrupt services.
4. Social engineering: Manipulating the LLM to behave differently than planned.