GenAI Red Teaming: Uncover GenAI risks and vulnerabilities in your LLM-based applications
Identify vulnerabilities in your homegrown applications powered by GenAI with Prompt Security’s Red Teaming
What is GenAI Red Teaming?
GenAI Red Teaming is an in-depth assessment technique, mimicking adversarial attacks on your GenAI applications to identify potential risks and vulnerabilities. As part of the process, the resilience of GenAI interfaces and applications is tested against a variety of threats, like Prompt Injection, Jailbreaks and Toxicity, ensuring they are safe and secure to face the external world.
Prompt’s Red Teaming
A team of world-class AI and Security experts will conduct comprehensive penetration testing based on state-of-the-art research in GenAI Security, guided by the OWASP Top 10 for LLMs and other industry frameworks, and using heavy compute resources.
Privilege Escalation
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.
AppSec / OWASP (llm08)
Privilege Escalation
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 emerging cybersecurity concern involves the potential misuse of LLM privileges to gain unauthorized access and control within an organization’s digital environment.
Key Concerns:
- Privilege Escalation: Unauthorized elevation of access rights.
- Unauthorized Data Access: Accessing sensitive data without proper authorization.
- System Compromise: Gaining control over systems beyond intended limits.
- Denial of Service: Disrupting services by overloading or manipulating systems.
AppSec / OWASP (llm08)
Brand Reputation Damage
Unregulated use of Generative AI (GenAI) poses a significant risk to brand reputation.
AppSec / OWASP (llm09)
Brand Reputation Damage
Unregulated use of Generative AI (GenAI) poses a significant risk to brand reputation. Inappropriate or off-brand content generated by GenAI applications can result in public relations challenges and harm the company's image.
Key Concerns:
- Embarrassing Content: Ensuring GenAI apps avoid generating toxic, sexual, biased, racist or offensive material.
- Competitive Disadvantage: Preventing GenAI apps from inadvertently promoting or supporting competitors.
- Off-Brand Behavior: Guaranteeing GenAI apps adhere to the desired behavior and communication pattern of the GenAI app and your brand.
AppSec / OWASP (llm09)
Prompt Injection
Prompt Injection is a cybersecurity threat where attackers manipulate a large language model (LLM) through carefully crafted inputs.
AppSec / OWASP (llm01)
Prompt Injection
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:
- Unauthorized data exfiltration: Extracting sensitive data without permission.
- Remote code execution: Running malicious code through the LLM.
- DDoS (Distributed Denial of Service): Overloading the system to disrupt services.
- Social engineering: Manipulating the LLM to behave differently than planned.
AppSec / OWASP (llm01)
Denial of Wallet / Service
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.
AppSec / OWASP (llm04)
Denial of Wallet / Service
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 OAI.
Key Concerns:
- Application Downtime: Risk of service unavailability due to resource overuse.
- Performance Degradation: Slower response times and reduced efficiency.
- Financial Implications: Potential for incurring high operational costs.
AppSec / OWASP (llm04)
Jailbreak
Jailbreaking represents a specific category of prompt injection where the goal is to coerce a generative GAI application into deviating from its intended behavior and established guidelines.
AppSec / OWASP (llm01)
Jailbreak
Jailbreaking represents a specific category of prompt injection where the goal is to coerce a generative GAI application into deviating from its intended behavior and established guidelines. This is typically achieved by crafting inputs that exploit system vulnerabilities, enabling responses without the usual restrictions or moderation. Notable examples include the widely discussed "Dan" or "Sydney" jailbreak incidents, where the AI systems responded without their usual constraints.
Key Concerns:
- Brand Reputation/Embarrassment: Preventing damage to the organization's public image due to unregulated AI behavior.
- Decreased Performance: Ensuring the generative AI application functions as designed, without unexpected deviations.
- Unsafe Customer Experience: Protecting users from potentially harmful or inappropriate interactions with the AI system.
AppSec / OWASP (llm01)
Prompt Leak
Prompt Leak is a specific form of prompt injection where a Large Language Model (LLM) inadvertently reveals its system instructions or internal logic.
AppSec / OWASP (llm01, llm06)
Prompt Leak
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 generative AI (GAI) application. As prompt engineering becomes increasingly integral to the development of GAI apps, any unintentional disclosure of these prompts can be considered as exposure of proprietary code or intellectual property.
Key Concerns:
- Intellectual Property Disclosure: Preventing the unauthorized revelation of proprietary information embedded in system prompts.
- Recon for Downstream Attacks: Avoiding the leak of system prompts which could serve as reconnaissance for more damaging prompt injections.
- Brand Reputation/Embarrassment: Protecting the organization's public image from the fallout of accidental prompt disclosure which might contain embarrassing information.
AppSec / OWASP (llm01, llm06)
Toxicity / Bias / Harmful
A jailbroken Large Language Model (LLM) behaving unpredictably can pose significant risks, potentially endangering an organization, its employees, or customers.
AppSec /IT / OWASP (llm09)
Toxicity / Bias / Harmful
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:
- Toxicity: Preventing harmful or offensive content.
- Bias: Ensuring fair and impartial interactions.
- Racism: Avoiding racially insensitive or discriminatory content.
- Brand Reputation: Maintaining a positive public image.
- Inappropriate Sexual Content: Filtering out unsuitable sexual material.
AppSec /IT / OWASP (llm09)
Privilege Escalation
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.
AppSec / OWASP (llm08)
Privilege Escalation
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 emerging cybersecurity concern involves the potential misuse of LLM privileges to gain unauthorized access and control within an organization’s digital environment.
Key Concerns:
- Privilege Escalation: Unauthorized elevation of access rights.
- Unauthorized Data Access: Accessing sensitive data without proper authorization.
- System Compromise: Gaining control over systems beyond intended limits.
- Denial of Service: Disrupting services by overloading or manipulating systems.
AppSec / OWASP (llm08)
Brand Reputation Damage
Unregulated use of Generative AI (GenAI) poses a significant risk to brand reputation.
AppSec / OWASP (llm09)
Brand Reputation Damage
Unregulated use of Generative AI (GenAI) poses a significant risk to brand reputation. Inappropriate or off-brand content generated by GenAI applications can result in public relations challenges and harm the company's image.
Key Concerns:
- Embarrassing Content: Ensuring GenAI apps avoid generating toxic, sexual, biased, racist or offensive material.
- Competitive Disadvantage: Preventing GenAI apps from inadvertently promoting or supporting competitors.
- Off-Brand Behavior: Guaranteeing GenAI apps adhere to the desired behavior and communication pattern of the GenAI app and your brand.
AppSec / OWASP (llm09)
Prompt Injection
Prompt Injection is a cybersecurity threat where attackers manipulate a large language model (LLM) through carefully crafted inputs.
AppSec / OWASP (llm01)
Prompt Injection
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:
- Unauthorized data exfiltration: Extracting sensitive data without permission.
- Remote code execution: Running malicious code through the LLM.
- DDoS (Distributed Denial of Service): Overloading the system to disrupt services.
- Social engineering: Manipulating the LLM to behave differently than planned.
AppSec / OWASP (llm01)
Denial of Wallet / Service
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.
AppSec / OWASP (llm04)
Denial of Wallet / Service
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 OAI.
Key Concerns:
- Application Downtime: Risk of service unavailability due to resource overuse.
- Performance Degradation: Slower response times and reduced efficiency.
- Financial Implications: Potential for incurring high operational costs.
AppSec / OWASP (llm04)
Jailbreak
Jailbreaking represents a specific category of prompt injection where the goal is to coerce a generative GAI application into deviating from its intended behavior and established guidelines.
AppSec / OWASP (llm01)
Jailbreak
Jailbreaking represents a specific category of prompt injection where the goal is to coerce a generative GAI application into deviating from its intended behavior and established guidelines. This is typically achieved by crafting inputs that exploit system vulnerabilities, enabling responses without the usual restrictions or moderation. Notable examples include the widely discussed "Dan" or "Sydney" jailbreak incidents, where the AI systems responded without their usual constraints.
Key Concerns:
- Brand Reputation/Embarrassment: Preventing damage to the organization's public image due to unregulated AI behavior.
- Decreased Performance: Ensuring the generative AI application functions as designed, without unexpected deviations.
- Unsafe Customer Experience: Protecting users from potentially harmful or inappropriate interactions with the AI system.
AppSec / OWASP (llm01)
Prompt Leak
Prompt Leak is a specific form of prompt injection where a Large Language Model (LLM) inadvertently reveals its system instructions or internal logic.
AppSec / OWASP (llm01, llm06)
Prompt Leak
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 generative AI (GAI) application. As prompt engineering becomes increasingly integral to the development of GAI apps, any unintentional disclosure of these prompts can be considered as exposure of proprietary code or intellectual property.
Key Concerns:
- Intellectual Property Disclosure: Preventing the unauthorized revelation of proprietary information embedded in system prompts.
- Recon for Downstream Attacks: Avoiding the leak of system prompts which could serve as reconnaissance for more damaging prompt injections.
- Brand Reputation/Embarrassment: Protecting the organization's public image from the fallout of accidental prompt disclosure which might contain embarrassing information.
AppSec / OWASP (llm01, llm06)
Toxicity / Bias / Harmful
A jailbroken Large Language Model (LLM) behaving unpredictably can pose significant risks, potentially endangering an organization, its employees, or customers.
AppSec /IT / OWASP (llm09)
Toxicity / Bias / Harmful
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:
- Toxicity: Preventing harmful or offensive content.
- Bias: Ensuring fair and impartial interactions.
- Racism: Avoiding racially insensitive or discriminatory content.
- Brand Reputation: Maintaining a positive public image.
- Inappropriate Sexual Content: Filtering out unsuitable sexual material.
AppSec /IT / OWASP (llm09)
Privilege Escalation
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.
AppSec / OWASP (llm08)
Privilege Escalation
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 emerging cybersecurity concern involves the potential misuse of LLM privileges to gain unauthorized access and control within an organization’s digital environment.
Key Concerns:
- Privilege Escalation: Unauthorized elevation of access rights.
- Unauthorized Data Access: Accessing sensitive data without proper authorization.
- System Compromise: Gaining control over systems beyond intended limits.
- Denial of Service: Disrupting services by overloading or manipulating systems.
AppSec / OWASP (llm08)
Brand Reputation Damage
Unregulated use of Generative AI (GenAI) poses a significant risk to brand reputation.
AppSec / OWASP (llm09)
Brand Reputation Damage
Unregulated use of Generative AI (GenAI) poses a significant risk to brand reputation. Inappropriate or off-brand content generated by GenAI applications can result in public relations challenges and harm the company's image.
Key Concerns:
- Embarrassing Content: Ensuring GenAI apps avoid generating toxic, sexual, biased, racist or offensive material.
- Competitive Disadvantage: Preventing GenAI apps from inadvertently promoting or supporting competitors.
- Off-Brand Behavior: Guaranteeing GenAI apps adhere to the desired behavior and communication pattern of the GenAI app and your brand.
AppSec / OWASP (llm09)
Prompt Injection
Prompt Injection is a cybersecurity threat where attackers manipulate a large language model (LLM) through carefully crafted inputs.
AppSec / OWASP (llm01)
Prompt Injection
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:
- Unauthorized data exfiltration: Extracting sensitive data without permission.
- Remote code execution: Running malicious code through the LLM.
- DDoS (Distributed Denial of Service): Overloading the system to disrupt services.
- Social engineering: Manipulating the LLM to behave differently than planned.
AppSec / OWASP (llm01)
Denial of Wallet / Service
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.
AppSec / OWASP (llm04)
Denial of Wallet / Service
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 OAI.
Key Concerns:
- Application Downtime: Risk of service unavailability due to resource overuse.
- Performance Degradation: Slower response times and reduced efficiency.
- Financial Implications: Potential for incurring high operational costs.
AppSec / OWASP (llm04)
Jailbreak
Jailbreaking represents a specific category of prompt injection where the goal is to coerce a generative GAI application into deviating from its intended behavior and established guidelines.
AppSec / OWASP (llm01)
Jailbreak
Jailbreaking represents a specific category of prompt injection where the goal is to coerce a generative GAI application into deviating from its intended behavior and established guidelines. This is typically achieved by crafting inputs that exploit system vulnerabilities, enabling responses without the usual restrictions or moderation. Notable examples include the widely discussed "Dan" or "Sydney" jailbreak incidents, where the AI systems responded without their usual constraints.
Key Concerns:
- Brand Reputation/Embarrassment: Preventing damage to the organization's public image due to unregulated AI behavior.
- Decreased Performance: Ensuring the generative AI application functions as designed, without unexpected deviations.
- Unsafe Customer Experience: Protecting users from potentially harmful or inappropriate interactions with the AI system.
AppSec / OWASP (llm01)
Prompt Leak
Prompt Leak is a specific form of prompt injection where a Large Language Model (LLM) inadvertently reveals its system instructions or internal logic.
AppSec / OWASP (llm01, llm06)
Prompt Leak
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 generative AI (GAI) application. As prompt engineering becomes increasingly integral to the development of GAI apps, any unintentional disclosure of these prompts can be considered as exposure of proprietary code or intellectual property.
Key Concerns:
- Intellectual Property Disclosure: Preventing the unauthorized revelation of proprietary information embedded in system prompts.
- Recon for Downstream Attacks: Avoiding the leak of system prompts which could serve as reconnaissance for more damaging prompt injections.
- Brand Reputation/Embarrassment: Protecting the organization's public image from the fallout of accidental prompt disclosure which might contain embarrassing information.
AppSec / OWASP (llm01, llm06)
Toxicity / Bias / Harmful
A jailbroken Large Language Model (LLM) behaving unpredictably can pose significant risks, potentially endangering an organization, its employees, or customers.
AppSec /IT / OWASP (llm09)
Toxicity / Bias / Harmful
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:
- Toxicity: Preventing harmful or offensive content.
- Bias: Ensuring fair and impartial interactions.
- Racism: Avoiding racially insensitive or discriminatory content.
- Brand Reputation: Maintaining a positive public image.
- Inappropriate Sexual Content: Filtering out unsuitable sexual material.
AppSec /IT / OWASP (llm09)
Privilege Escalation
AppSec / OWASP (llm08)
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 emerging cybersecurity concern involves the potential misuse of LLM privileges to gain unauthorized access and control within an organization’s digital environment.
Key Concerns:
- Privilege Escalation: Unauthorized elevation of access rights.
- Unauthorized Data Access: Accessing sensitive data without proper authorization.
- System Compromise: Gaining control over systems beyond intended limits.
- Denial of Service: Disrupting services by overloading or manipulating systems.
How
Helps:
To mitigate these risks, our platform incorporates robust security protocols designed to prevent privilege escalation. Recognizing that architectural imperfections and over-privileged roles can exist, our system actively monitors and blocks any prompts that may lead to unwarranted access to critical components within your environment. In the event of such an attempt, our system not only blocks the action but also immediately alerts your security team, thus ensuring a higher level of safeguarding against privilege escalation threats.
Brand Reputation Damage
AppSec / OWASP (llm09)
Unregulated use of Generative AI (GenAI) poses a significant risk to brand reputation. Inappropriate or off-brand content generated by GenAI applications can result in public relations challenges and harm the company's image.
Key Concerns:
- Embarrassing Content: Ensuring GenAI apps avoid generating toxic, sexual, biased, racist or offensive material.
- Competitive Disadvantage: Preventing GenAI apps from inadvertently promoting or supporting competitors.
- Off-Brand Behavior: Guaranteeing GenAI apps adhere to the desired behavior and communication pattern of the GenAI app and your brand.
How
Helps:
To mitigate these risks, our platform rigorously supervises each input and output of your GenAI applications. This vigilant monitoring ensures that your GenAI apps consistently follow your guidelines, producing relevant and appropriate responses. We aim to prevent any negative exposure on social media platforms like Twitter, safeguarding your brand's integrity and public image.
Prompt Injection
AppSec / OWASP (llm01)
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:
- Unauthorized data exfiltration: Extracting sensitive data without permission.
- Remote code execution: Running malicious code through the LLM.
- DDoS (Distributed Denial of Service): Overloading the system to disrupt services.
- Social engineering: Manipulating the LLM to behave differently than planned.
How
Helps:
To combat this, our platform employs a sophisticated AI engine that detects and blocks adversarial prompt injection attempts in real-time. This system ensures minimal latency overhead, with a response time below 200 milliseconds for 95% of cases. In the event of an attempted attack, besides blocking, the platform immediately sends an alert to the our dashboard, providing robust protection against this emerging cybersecurity threat.
Denial of Wallet / Service
AppSec / OWASP (llm04)
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 OAI.
Key Concerns:
- Application Downtime: Risk of service unavailability due to resource overuse.
- Performance Degradation: Slower response times and reduced efficiency.
- Financial Implications: Potential for incurring high operational costs.
How
Helps:
To address these threats, our platform employs robust measures to ensure each interaction with the GAI application is legitimate and secure. We closely monitor for any abnormal usage or increased activity from specific identities, and promptly block them if they deviate from normal parameters. This proactive approach guarantees the integrity of your application, protecting it from attacks that could lead to service interruptions or excessive costs. Rest assured, our system vigilantly safeguards against these emerging security challenges.
Jailbreak
AppSec / OWASP (llm01)
Jailbreaking represents a specific category of prompt injection where the goal is to coerce a generative GAI application into deviating from its intended behavior and established guidelines. This is typically achieved by crafting inputs that exploit system vulnerabilities, enabling responses without the usual restrictions or moderation. Notable examples include the widely discussed "Dan" or "Sydney" jailbreak incidents, where the AI systems responded without their usual constraints.
Key Concerns:
- Brand Reputation/Embarrassment: Preventing damage to the organization's public image due to unregulated AI behavior.
- Decreased Performance: Ensuring the generative AI application functions as designed, without unexpected deviations.
- Unsafe Customer Experience: Protecting users from potentially harmful or inappropriate interactions with the AI system.
How
Helps:
To mitigate these risks, our platform diligently monitors and analyzes each prompt and response. This continuous scrutiny is designed to detect any attempts of jailbreaking, ensuring that the generative AI application remains aligned with its intended operational parameters and exhibits behavior that is safe, reliable, and consistent with organizational standards.
Prompt Leak
AppSec / OWASP (llm01, llm06)
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 generative AI (GAI) application. As prompt engineering becomes increasingly integral to the development of GAI apps, any unintentional disclosure of these prompts can be considered as exposure of proprietary code or intellectual property.
Key Concerns:
- Intellectual Property Disclosure: Preventing the unauthorized revelation of proprietary information embedded in system prompts.
- Recon for Downstream Attacks: Avoiding the leak of system prompts which could serve as reconnaissance for more damaging prompt injections.
- Brand Reputation/Embarrassment: Protecting the organization's public image from the fallout of accidental prompt disclosure which might contain embarrassing information.
How
Helps:
To address this, our platform meticulously monitors each prompt and response to ensure that the GenAI app does not inadvertently disclose its assigned instructions, policies, or system prompts. In the event of a potential leak, our system promptly intervenes, blocking the attempt and issuing a corresponding alert. This proactive approach fortifies your platform against the risks associated with prompt leak, safeguarding both your intellectual property and brand's integrity.
Toxicity / Bias / Harmful
AppSec /IT / OWASP (llm09)
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:
- Toxicity: Preventing harmful or offensive content.
- Bias: Ensuring fair and impartial interactions.
- Racism: Avoiding racially insensitive or discriminatory content.
- Brand Reputation: Maintaining a positive public image.
- Inappropriate Sexual Content: Filtering out unsuitable sexual material.
How
Helps:
Our platform scrutinizes every response generated by LLMs before it reaches a customer or employee. This ensures all interactions are appropriate and non-harmful. We employ extensive moderation filters covering a broad range of topics, ensuring your customers and employees have a positive experience with your product while maintaining your brand's impeccable reputation.
Privilege Escalation
AppSec / OWASP (llm08)
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 emerging cybersecurity concern involves the potential misuse of LLM privileges to gain unauthorized access and control within an organization’s digital environment.
Key Concerns:
- Privilege Escalation: Unauthorized elevation of access rights.
- Unauthorized Data Access: Accessing sensitive data without proper authorization.
- System Compromise: Gaining control over systems beyond intended limits.
- Denial of Service: Disrupting services by overloading or manipulating systems.
How
Helps:
To mitigate these risks, our platform incorporates robust security protocols designed to prevent privilege escalation. Recognizing that architectural imperfections and over-privileged roles can exist, our system actively monitors and blocks any prompts that may lead to unwarranted access to critical components within your environment. In the event of such an attempt, our system not only blocks the action but also immediately alerts your security team, thus ensuring a higher level of safeguarding against privilege escalation threats.
Brand Reputation Damage
AppSec / OWASP (llm09)
Unregulated use of Generative AI (GenAI) poses a significant risk to brand reputation. Inappropriate or off-brand content generated by GenAI applications can result in public relations challenges and harm the company's image.
Key Concerns:
- Embarrassing Content: Ensuring GenAI apps avoid generating toxic, sexual, biased, racist or offensive material.
- Competitive Disadvantage: Preventing GenAI apps from inadvertently promoting or supporting competitors.
- Off-Brand Behavior: Guaranteeing GenAI apps adhere to the desired behavior and communication pattern of the GenAI app and your brand.
How
Helps:
To mitigate these risks, our platform rigorously supervises each input and output of your GenAI applications. This vigilant monitoring ensures that your GenAI apps consistently follow your guidelines, producing relevant and appropriate responses. We aim to prevent any negative exposure on social media platforms like Twitter, safeguarding your brand's integrity and public image.
Prompt Injection
AppSec / OWASP (llm01)
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:
- Unauthorized data exfiltration: Extracting sensitive data without permission.
- Remote code execution: Running malicious code through the LLM.
- DDoS (Distributed Denial of Service): Overloading the system to disrupt services.
- Social engineering: Manipulating the LLM to behave differently than planned.
How
Helps:
To combat this, our platform employs a sophisticated AI engine that detects and blocks adversarial prompt injection attempts in real-time. This system ensures minimal latency overhead, with a response time below 200 milliseconds for 95% of cases. In the event of an attempted attack, besides blocking, the platform immediately sends an alert to the our dashboard, providing robust protection against this emerging cybersecurity threat.
Denial of Wallet / Service
AppSec / OWASP (llm04)
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 OAI.
Key Concerns:
- Application Downtime: Risk of service unavailability due to resource overuse.
- Performance Degradation: Slower response times and reduced efficiency.
- Financial Implications: Potential for incurring high operational costs.
How
Helps:
To address these threats, our platform employs robust measures to ensure each interaction with the GAI application is legitimate and secure. We closely monitor for any abnormal usage or increased activity from specific identities, and promptly block them if they deviate from normal parameters. This proactive approach guarantees the integrity of your application, protecting it from attacks that could lead to service interruptions or excessive costs. Rest assured, our system vigilantly safeguards against these emerging security challenges.
Jailbreak
AppSec / OWASP (llm01)
Jailbreaking represents a specific category of prompt injection where the goal is to coerce a generative GAI application into deviating from its intended behavior and established guidelines. This is typically achieved by crafting inputs that exploit system vulnerabilities, enabling responses without the usual restrictions or moderation. Notable examples include the widely discussed "Dan" or "Sydney" jailbreak incidents, where the AI systems responded without their usual constraints.
Key Concerns:
- Brand Reputation/Embarrassment: Preventing damage to the organization's public image due to unregulated AI behavior.
- Decreased Performance: Ensuring the generative AI application functions as designed, without unexpected deviations.
- Unsafe Customer Experience: Protecting users from potentially harmful or inappropriate interactions with the AI system.
How
Helps:
To mitigate these risks, our platform diligently monitors and analyzes each prompt and response. This continuous scrutiny is designed to detect any attempts of jailbreaking, ensuring that the generative AI application remains aligned with its intended operational parameters and exhibits behavior that is safe, reliable, and consistent with organizational standards.
Prompt Leak
AppSec / OWASP (llm01, llm06)
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 generative AI (GAI) application. As prompt engineering becomes increasingly integral to the development of GAI apps, any unintentional disclosure of these prompts can be considered as exposure of proprietary code or intellectual property.
Key Concerns:
- Intellectual Property Disclosure: Preventing the unauthorized revelation of proprietary information embedded in system prompts.
- Recon for Downstream Attacks: Avoiding the leak of system prompts which could serve as reconnaissance for more damaging prompt injections.
- Brand Reputation/Embarrassment: Protecting the organization's public image from the fallout of accidental prompt disclosure which might contain embarrassing information.
How
Helps:
To address this, our platform meticulously monitors each prompt and response to ensure that the GenAI app does not inadvertently disclose its assigned instructions, policies, or system prompts. In the event of a potential leak, our system promptly intervenes, blocking the attempt and issuing a corresponding alert. This proactive approach fortifies your platform against the risks associated with prompt leak, safeguarding both your intellectual property and brand's integrity.
Toxicity / Bias / Harmful
AppSec /IT / OWASP (llm09)
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:
- Toxicity: Preventing harmful or offensive content.
- Bias: Ensuring fair and impartial interactions.
- Racism: Avoiding racially insensitive or discriminatory content.
- Brand Reputation: Maintaining a positive public image.
- Inappropriate Sexual Content: Filtering out unsuitable sexual material.
How
Helps:
Our platform scrutinizes every response generated by LLMs before it reaches a customer or employee. This ensures all interactions are appropriate and non-harmful. We employ extensive moderation filters covering a broad range of topics, ensuring your customers and employees have a positive experience with your product while maintaining your brand's impeccable reputation.
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Prompt Fuzzer
Test and harden the system prompt of your GenAI Apps
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