At SplxAI, we are driven by our commitment to make AI safe and trustworthy for everyone. Therefore, we are thrilled to announce our strategic partnership with Lasso Security to offer a comprehensive, combined security solution of Red and Blue Teaming to organizations looking to leverage GenAI technologies safely.
As Generative AI (GenAI) is rapidly transforming industries, it introduces a new kind of cybersecurity challenges. While the potential of GenAI is vast, the swift adoption of these technologies opens many doors to cybercriminals to exploit AI vulnerabilities, leading to potential breaches, brand damage, and legal risks. To fully harness the power of GenAI, organizations must implement strong security strategies that protect against these emerging threats and safeguard their reputation. Balancing innovation with robust security is now more critical than ever in the era of AI-driven business solutions.
At SplxAI, we are driven by our commitment to make AI safe and trustworthy for everyone. Therefore, we are thrilled to announce our strategic partnership with Lasso Security to offer a comprehensive, combined security solution of Red and Blue Teaming to organizations looking to leverage GenAI technologies safely.
As Generative AI (GenAI) is rapidly transforming industries, it introduces a new kind of cybersecurity challenges. While the potential of GenAI is vast, the swift adoption of these technologies opens many doors to cybercriminals to exploit AI vulnerabilities, leading to potential breaches, brand damage, and legal risks. To fully harness the power of GenAI, organizations must implement strong security strategies that protect against these emerging threats and safeguard their reputation. Balancing innovation with robust security is now more critical than ever in the era of AI-driven business solutions.
At SplxAI, we are driven by our commitment to make AI safe and trustworthy for everyone. Therefore, we are thrilled to announce our strategic partnership with Lasso Security to offer a comprehensive, combined security solution of Red and Blue Teaming to organizations looking to leverage GenAI technologies safely.
As Generative AI (GenAI) is rapidly transforming industries, it introduces a new kind of cybersecurity challenges. While the potential of GenAI is vast, the swift adoption of these technologies opens many doors to cybercriminals to exploit AI vulnerabilities, leading to potential breaches, brand damage, and legal risks. To fully harness the power of GenAI, organizations must implement strong security strategies that protect against these emerging threats and safeguard their reputation. Balancing innovation with robust security is now more critical than ever in the era of AI-driven business solutions.
Understanding the GenAI Security Landscape
The data speaks for itself:
Over 1.5 billion global users actively engage with conversational AI tools.
45% of companies hold back on chatbot adoption due to concerns over privacy, security, and legal risks.
64% of potential users would embrace GenAI if they felt it were more secure.
Chatbot attacks linked to GenAI vulnerabilities have surged 4X annually.
These statistics highlight the critical need for robust, proactive security strategies to safeguard GenAI deployments from ever-evolving attack vectors.
The data speaks for itself:
Over 1.5 billion global users actively engage with conversational AI tools.
45% of companies hold back on chatbot adoption due to concerns over privacy, security, and legal risks.
64% of potential users would embrace GenAI if they felt it were more secure.
Chatbot attacks linked to GenAI vulnerabilities have surged 4X annually.
These statistics highlight the critical need for robust, proactive security strategies to safeguard GenAI deployments from ever-evolving attack vectors.
The data speaks for itself:
Over 1.5 billion global users actively engage with conversational AI tools.
45% of companies hold back on chatbot adoption due to concerns over privacy, security, and legal risks.
64% of potential users would embrace GenAI if they felt it were more secure.
Chatbot attacks linked to GenAI vulnerabilities have surged 4X annually.
These statistics highlight the critical need for robust, proactive security strategies to safeguard GenAI deployments from ever-evolving attack vectors.
The Challenges: Navigating the threat landscape of GenAI
Generative AI (GenAI) brings unique risks that require vigilant oversight. Emerging reports from OWASP and leading researchers have revealed significant vulnerabilities within Large Language Models (LLMs), emphasizing the need for proactive security measures to mitigate these threats.
Prompt Injection
Description: Malicious inputs causing unintended outputs.
Example: A disguised command in a chatbot revealing sensitive info.A disguised command in a chatbot revealing sensitive info.
How to identify: Simulate adversarial attack scenarios with automated AI Pentesting.
How to prevent: Input validation, strict input guidelines, context-aware filtering.
Insecure Output Handling
Description: Failing to sanitize or validate outputs, leading to attacks.
Example: An LLM-generated script causing an XSS attack.
How to identify: Based on your policies and guidelines, identify vulnerabilities in outputs with AI Red Teaming.
How to prevent: Sanitize outputs, encode and escape outputs.
Training Data Poisoning
Description: Manipulation of training data to skew model behavior.
Example: Biased data causing a financial LLM to recommend poor investments.
How to identify: Test the AI model’s behavior with domain-specific attack scenarios to detect anomalies.
How to prevent: Validate and clean data, and detect anomalies.
Model Denial of Service
Description: Overwhelming the LLM with requests causing slowdowns or unavailability.
Example: Resource-intensive queries overloading the system.
How to identify: Perform automated AI pentesting and manual AI Red Teaming tailored for your model and system.
How to prevent: Rate limiting, monitor queues, optimize performance, and scaling strategies.
Supply Chain Vulnerabilities
Description: Compromised third-party services or resources affecting the model.
Example: Tampered data from a third-party provider manipulating outputs.
How to identify: Use SBOMs and AI SAST scanners. Track vulnerabilities in frameworks integrated into your AI system.
How to prevent: Security audits, monitor behavior, trusted supply chain.
Sensitive Information Disclosure
Description: LLMs revealing confidential data from training materials.
Example: Responses containing PII due to overfitting on the training data.
How to identify: Simulate regular and adversarial user conversations focused on PII extraction. Do AI Red Teaming.
How to prevent: Anonymize data, and enforce access control with AI Firewall.
Insecure Plugin Design
Description: Vulnerabilities in plugins or extensions.
Example: A third-party plugin causing SQL injections.
How to identify: Do tailored AI Red Teaming for each integrated plugin or extension.
How to prevent: Do security reviews and follow coding standards.
Excessive Agency
Description: LLMs making uncontrolled decisions.
Example: An LLM customer service tool making unauthorized refunds.
How to identify: Do SAST and DAST analysis of decision right in AI Model.
How to prevent: Limit LLM decisions and provide human oversight.
Overreliance
Description: Overreliance on LLMs for critical decisions without human oversight.
Example: An LLM making errors in customer service decisions without review.
How to identify: Identify skill gaps of system users and their AI Safety Awareness.
How to prevent: Human review of LLM outputs, supplemented with data inputs and AI security awareness education.
Model Theft
Description: Unauthorized access to proprietary LLM models.
Example: A competitor downloading and using an LLM illicitly.
How to identify: Perform AI Model Red Teaming.
How to prevent: Authentication, encrypt data, and access control.
Generative AI (GenAI) brings unique risks that require vigilant oversight. Emerging reports from OWASP and leading researchers have revealed significant vulnerabilities within Large Language Models (LLMs), emphasizing the need for proactive security measures to mitigate these threats.
Prompt Injection
Description: Malicious inputs causing unintended outputs.
Example: A disguised command in a chatbot revealing sensitive info.A disguised command in a chatbot revealing sensitive info.
How to identify: Simulate adversarial attack scenarios with automated AI Pentesting.
How to prevent: Input validation, strict input guidelines, context-aware filtering.
Insecure Output Handling
Description: Failing to sanitize or validate outputs, leading to attacks.
Example: An LLM-generated script causing an XSS attack.
How to identify: Based on your policies and guidelines, identify vulnerabilities in outputs with AI Red Teaming.
How to prevent: Sanitize outputs, encode and escape outputs.
Training Data Poisoning
Description: Manipulation of training data to skew model behavior.
Example: Biased data causing a financial LLM to recommend poor investments.
How to identify: Test the AI model’s behavior with domain-specific attack scenarios to detect anomalies.
How to prevent: Validate and clean data, and detect anomalies.
Model Denial of Service
Description: Overwhelming the LLM with requests causing slowdowns or unavailability.
Example: Resource-intensive queries overloading the system.
How to identify: Perform automated AI pentesting and manual AI Red Teaming tailored for your model and system.
How to prevent: Rate limiting, monitor queues, optimize performance, and scaling strategies.
Supply Chain Vulnerabilities
Description: Compromised third-party services or resources affecting the model.
Example: Tampered data from a third-party provider manipulating outputs.
How to identify: Use SBOMs and AI SAST scanners. Track vulnerabilities in frameworks integrated into your AI system.
How to prevent: Security audits, monitor behavior, trusted supply chain.
Sensitive Information Disclosure
Description: LLMs revealing confidential data from training materials.
Example: Responses containing PII due to overfitting on the training data.
How to identify: Simulate regular and adversarial user conversations focused on PII extraction. Do AI Red Teaming.
How to prevent: Anonymize data, and enforce access control with AI Firewall.
Insecure Plugin Design
Description: Vulnerabilities in plugins or extensions.
Example: A third-party plugin causing SQL injections.
How to identify: Do tailored AI Red Teaming for each integrated plugin or extension.
How to prevent: Do security reviews and follow coding standards.
Excessive Agency
Description: LLMs making uncontrolled decisions.
Example: An LLM customer service tool making unauthorized refunds.
How to identify: Do SAST and DAST analysis of decision right in AI Model.
How to prevent: Limit LLM decisions and provide human oversight.
Overreliance
Description: Overreliance on LLMs for critical decisions without human oversight.
Example: An LLM making errors in customer service decisions without review.
How to identify: Identify skill gaps of system users and their AI Safety Awareness.
How to prevent: Human review of LLM outputs, supplemented with data inputs and AI security awareness education.
Model Theft
Description: Unauthorized access to proprietary LLM models.
Example: A competitor downloading and using an LLM illicitly.
How to identify: Perform AI Model Red Teaming.
How to prevent: Authentication, encrypt data, and access control.
Generative AI (GenAI) brings unique risks that require vigilant oversight. Emerging reports from OWASP and leading researchers have revealed significant vulnerabilities within Large Language Models (LLMs), emphasizing the need for proactive security measures to mitigate these threats.
Prompt Injection
Description: Malicious inputs causing unintended outputs.
Example: A disguised command in a chatbot revealing sensitive info.A disguised command in a chatbot revealing sensitive info.
How to identify: Simulate adversarial attack scenarios with automated AI Pentesting.
How to prevent: Input validation, strict input guidelines, context-aware filtering.
Insecure Output Handling
Description: Failing to sanitize or validate outputs, leading to attacks.
Example: An LLM-generated script causing an XSS attack.
How to identify: Based on your policies and guidelines, identify vulnerabilities in outputs with AI Red Teaming.
How to prevent: Sanitize outputs, encode and escape outputs.
Training Data Poisoning
Description: Manipulation of training data to skew model behavior.
Example: Biased data causing a financial LLM to recommend poor investments.
How to identify: Test the AI model’s behavior with domain-specific attack scenarios to detect anomalies.
How to prevent: Validate and clean data, and detect anomalies.
Model Denial of Service
Description: Overwhelming the LLM with requests causing slowdowns or unavailability.
Example: Resource-intensive queries overloading the system.
How to identify: Perform automated AI pentesting and manual AI Red Teaming tailored for your model and system.
How to prevent: Rate limiting, monitor queues, optimize performance, and scaling strategies.
Supply Chain Vulnerabilities
Description: Compromised third-party services or resources affecting the model.
Example: Tampered data from a third-party provider manipulating outputs.
How to identify: Use SBOMs and AI SAST scanners. Track vulnerabilities in frameworks integrated into your AI system.
How to prevent: Security audits, monitor behavior, trusted supply chain.
Sensitive Information Disclosure
Description: LLMs revealing confidential data from training materials.
Example: Responses containing PII due to overfitting on the training data.
How to identify: Simulate regular and adversarial user conversations focused on PII extraction. Do AI Red Teaming.
How to prevent: Anonymize data, and enforce access control with AI Firewall.
Insecure Plugin Design
Description: Vulnerabilities in plugins or extensions.
Example: A third-party plugin causing SQL injections.
How to identify: Do tailored AI Red Teaming for each integrated plugin or extension.
How to prevent: Do security reviews and follow coding standards.
Excessive Agency
Description: LLMs making uncontrolled decisions.
Example: An LLM customer service tool making unauthorized refunds.
How to identify: Do SAST and DAST analysis of decision right in AI Model.
How to prevent: Limit LLM decisions and provide human oversight.
Overreliance
Description: Overreliance on LLMs for critical decisions without human oversight.
Example: An LLM making errors in customer service decisions without review.
How to identify: Identify skill gaps of system users and their AI Safety Awareness.
How to prevent: Human review of LLM outputs, supplemented with data inputs and AI security awareness education.
Model Theft
Description: Unauthorized access to proprietary LLM models.
Example: A competitor downloading and using an LLM illicitly.
How to identify: Perform AI Model Red Teaming.
How to prevent: Authentication, encrypt data, and access control.
The Solution: Red and Blue Teaming for Complete Protection
With the combined strengths of Lasso Security and SplxAI, we are delivering a comprehensive "Purple Teaming" approach, integrating both redteaming and blueteaming to secure GenAI applications. Here's how our partnership addresses AI security challenges at every level:
Red Teaming with SplxAI: Offensive Security
SplxAI’s red teaming services simulate real-world cyberattacks to uncover and exploit vulnerabilities within GenAI environments. Our platform automates vulnerability scanning, saving organizations time and resources by taking away the months of manual testing usually required. These attack simulations are designed to mirror the tactics, techniques, and procedures (TTPs) employed by attackers, giving companies insights into the security gaps within their AI systems.
Key Red Teaming Services:
Automated Scanning: Continuous on-demand testing for GenAI-specific threats, such as prompt injection, off-topic usage, and hallucination
Compliance Mapping: Ensure alignment with major AI security frameworks, including OWASP LLM Top 10, MITRE ATLAS, ISO 42001, and others.
Comprehensive Reporting: Actionable insights into vulnerabilities, their potential impacts, and remediation steps.
Continuous Improvement: Iterative assessments to stay ahead of new threats.
Blue Teaming with Lasso Security: Defensive Security
Lasso Security’s blue teaming solutions complement SplxAI’s offensive approach by focusing on defending and mitigating all existing and emerging GenAI threats. From real-time threat response to automated mitigation, Lasso Security ensures that organizations remain secure at every stage of their GenAI deployment.
Key Blue Teaming Services:
Always-on Shadow LLM™: Uncover every LLM interaction, allowing precise identification of active tools, models, and users within an organization.
Real-Time Response and Automated Mitigation: Swift alerts and automated defenses ensure rapid responses to real-time threats.
Tailored Policy Enforcement: Organizations can implement customized security policies that align with their unique regulatory requirements.
Privacy Risk Reduction: Data protection is prioritized from the initial deployment stages, ensuring long-term security compliance.
With the combined strengths of Lasso Security and SplxAI, we are delivering a comprehensive "Purple Teaming" approach, integrating both redteaming and blueteaming to secure GenAI applications. Here's how our partnership addresses AI security challenges at every level:
Red Teaming with SplxAI: Offensive Security
SplxAI’s red teaming services simulate real-world cyberattacks to uncover and exploit vulnerabilities within GenAI environments. Our platform automates vulnerability scanning, saving organizations time and resources by taking away the months of manual testing usually required. These attack simulations are designed to mirror the tactics, techniques, and procedures (TTPs) employed by attackers, giving companies insights into the security gaps within their AI systems.
Key Red Teaming Services:
Automated Scanning: Continuous on-demand testing for GenAI-specific threats, such as prompt injection, off-topic usage, and hallucination
Compliance Mapping: Ensure alignment with major AI security frameworks, including OWASP LLM Top 10, MITRE ATLAS, ISO 42001, and others.
Comprehensive Reporting: Actionable insights into vulnerabilities, their potential impacts, and remediation steps.
Continuous Improvement: Iterative assessments to stay ahead of new threats.
Blue Teaming with Lasso Security: Defensive Security
Lasso Security’s blue teaming solutions complement SplxAI’s offensive approach by focusing on defending and mitigating all existing and emerging GenAI threats. From real-time threat response to automated mitigation, Lasso Security ensures that organizations remain secure at every stage of their GenAI deployment.
Key Blue Teaming Services:
Always-on Shadow LLM™: Uncover every LLM interaction, allowing precise identification of active tools, models, and users within an organization.
Real-Time Response and Automated Mitigation: Swift alerts and automated defenses ensure rapid responses to real-time threats.
Tailored Policy Enforcement: Organizations can implement customized security policies that align with their unique regulatory requirements.
Privacy Risk Reduction: Data protection is prioritized from the initial deployment stages, ensuring long-term security compliance.
With the combined strengths of Lasso Security and SplxAI, we are delivering a comprehensive "Purple Teaming" approach, integrating both redteaming and blueteaming to secure GenAI applications. Here's how our partnership addresses AI security challenges at every level:
Red Teaming with SplxAI: Offensive Security
SplxAI’s red teaming services simulate real-world cyberattacks to uncover and exploit vulnerabilities within GenAI environments. Our platform automates vulnerability scanning, saving organizations time and resources by taking away the months of manual testing usually required. These attack simulations are designed to mirror the tactics, techniques, and procedures (TTPs) employed by attackers, giving companies insights into the security gaps within their AI systems.
Key Red Teaming Services:
Automated Scanning: Continuous on-demand testing for GenAI-specific threats, such as prompt injection, off-topic usage, and hallucination
Compliance Mapping: Ensure alignment with major AI security frameworks, including OWASP LLM Top 10, MITRE ATLAS, ISO 42001, and others.
Comprehensive Reporting: Actionable insights into vulnerabilities, their potential impacts, and remediation steps.
Continuous Improvement: Iterative assessments to stay ahead of new threats.
Blue Teaming with Lasso Security: Defensive Security
Lasso Security’s blue teaming solutions complement SplxAI’s offensive approach by focusing on defending and mitigating all existing and emerging GenAI threats. From real-time threat response to automated mitigation, Lasso Security ensures that organizations remain secure at every stage of their GenAI deployment.
Key Blue Teaming Services:
Always-on Shadow LLM™: Uncover every LLM interaction, allowing precise identification of active tools, models, and users within an organization.
Real-Time Response and Automated Mitigation: Swift alerts and automated defenses ensure rapid responses to real-time threats.
Tailored Policy Enforcement: Organizations can implement customized security policies that align with their unique regulatory requirements.
Privacy Risk Reduction: Data protection is prioritized from the initial deployment stages, ensuring long-term security compliance.
The Power of Purple Teaming: Stronger Together
By combining red and blue teaming into a unified, purple teaming strategy, SplxAI and Lasso Security provide a more resilient defense posture for organizations in the GenAI space. This collaborative approach strengthens AI security in several critical areas:
Enhanced Security: Offensive and defensive measures ensure that every potential vulnerability is addressed.
Strategic Insights: Informed decision-making for long-term security investments and planning.
Continuous Risk Management: Ongoing assessment and improvement for proactive risk mitigation.
Compliance Alignment: Consistent adherence to industry regulations and frameworks.
By leveraging the combined expertise of SplxAI’s red teaming and Lasso Security’s blue teaming, organizations can confidently embrace GenAI technologies, turning potential risks into strategic advantages. As AI continues to shape the future, SplxAI and Lasso Security are here to ensure that your organization’s security evolves with it.
Learn more about how our red teaming and blue teaming synergy can help you secure your GenAI apps by downloading our solution brief.
By combining red and blue teaming into a unified, purple teaming strategy, SplxAI and Lasso Security provide a more resilient defense posture for organizations in the GenAI space. This collaborative approach strengthens AI security in several critical areas:
Enhanced Security: Offensive and defensive measures ensure that every potential vulnerability is addressed.
Strategic Insights: Informed decision-making for long-term security investments and planning.
Continuous Risk Management: Ongoing assessment and improvement for proactive risk mitigation.
Compliance Alignment: Consistent adherence to industry regulations and frameworks.
By leveraging the combined expertise of SplxAI’s red teaming and Lasso Security’s blue teaming, organizations can confidently embrace GenAI technologies, turning potential risks into strategic advantages. As AI continues to shape the future, SplxAI and Lasso Security are here to ensure that your organization’s security evolves with it.
Learn more about how our red teaming and blue teaming synergy can help you secure your GenAI apps by downloading our solution brief.
By combining red and blue teaming into a unified, purple teaming strategy, SplxAI and Lasso Security provide a more resilient defense posture for organizations in the GenAI space. This collaborative approach strengthens AI security in several critical areas:
Enhanced Security: Offensive and defensive measures ensure that every potential vulnerability is addressed.
Strategic Insights: Informed decision-making for long-term security investments and planning.
Continuous Risk Management: Ongoing assessment and improvement for proactive risk mitigation.
Compliance Alignment: Consistent adherence to industry regulations and frameworks.
By leveraging the combined expertise of SplxAI’s red teaming and Lasso Security’s blue teaming, organizations can confidently embrace GenAI technologies, turning potential risks into strategic advantages. As AI continues to shape the future, SplxAI and Lasso Security are here to ensure that your organization’s security evolves with it.
Learn more about how our red teaming and blue teaming synergy can help you secure your GenAI apps by downloading our solution brief.
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