GenAI Red Teaming: Uncover GenAI risks and vulnerabilities in your LLM-based applications

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.

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.

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

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.

Denial of Wallet Attacks, alongside Denial of Service, are critical security concerns where an attacker excessively engages with a Large Language Model (LLM) applications, 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

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.

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.

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.

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.

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

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.

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.

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.

Denial of Wallet Attacks, alongside Denial of Service, are critical security concerns where an attacker excessively engages with a Large Language Model (LLM) applications, 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

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.

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.

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) applications, 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

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.