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AI Red Teaming (AIRT)

AI Red Teaming (AIRT) is an offensive security engagement conducted by security experts to simulate cyber attacks against GenAI application.
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Improved Model Robustness
Expose vulnerabilities in AI models, such as susceptibility to adversarial attacks, allowing developers to refine and strengthen the models against unexpected or malicious inputs.
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Enhanced Security Posture
Proactively identify and address security weaknesses to ensure that AI systems are better protected against real-world threats, enhancing overall system security.
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Increased Trust and Reliability
rigorously test AI systems under adversarial conditions to increase the trustworthiness and reliability of your AI applications, making them safer for real-world deployment.
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Deep Learning Insights
Get valuable insights into the behavior of AI models under adversarial conditions, highlighting areas for improvement in both the model's design and its deployment environment, thus aiding in the development of more resilient AI systems.
Process
STEP 0
Pre-Engagement
Rules of Engagement
 Scope Definition
 Greatest Risk Objectives
 Emergency Contacts
 Specific Timelines / Flexibilities
 Disaster Recovery Procedures
STEP 1
Security Assessment
In-Person Executive Presentations In-Person Technical Presentations Cybersecurity Consultations Industry Knowledge Sharing Follow-Up Penetration Testing Networking Opportunities
STEP 2
Documentation
Rules of Engagement
 Scope Definition
 Greatest Risk Objectives
 Emergency Contacts
 Specific Timelines / Flexibilities
 Disaster Recovery Procedures
STEP 3
Success Ops
In-Person Executive Presentations In-Person Technical Presentations Cybersecurity Consultations Industry Knowledge Sharing Follow-Up Penetration Testing Networking Opportunities
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Malicious Prompt Engineering

Prompt engineering refers to the process of crafting messages to successfully steer an LLM toward executing a particular task. Since prompts are user-supplied input, such input may include malicious instructions that cause unintended behaviors in the model. For instance, an attacker can guide a model to output malicious instructions such as participating in illegal activities, crafting and distributing malicious software, creating websites that promote racist activities, etc.
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AMPE

Automated Malicious Prompt Engineering (AMPE) is an application designed to automatically create and deploy malicious prompts against GPT text generation models.