AI Pentesting
Secure Your AI Systems with Advanced AI Penetration Testing
Overview: AI Pentesting
AI systems are now core to business operations, decision-making, and customer interactions. That also makes them a high-value attack surface. AI Pentesting helps organizations identify, exploit, and remediate security weaknesses unique to AI-driven systems before attackers do.
AI Pentesting - What Exactly You Need to Know
AI Penetration Testing, or AI Pentesting, is the practice of emulating real-world adversarial attacks against artificial intelligence systems, including machine learning models, large language models (LLMs), generative AI applications, chatbots, and AI-powered APIs.
AI-Specific Risks
Prompt injection and jailbreak attacks
Training data and model poisoning
Sensitive data leakage through AI outputs
Insecure model integrations and APIs
Abuse of AI logic, memory, and context
Unauthorized model access or theft
Why AI Pentesting Focuses on These Risks?
AI systems continuously learn, adapt, and interact with users. AI Pentesting is designed to evaluate how AI behaves under malicious inputs, adversarial conditions, and real-world misuse scenarios.
AI Pentesting Methodology
Kratikal’s AI Pentesting methodology is aligned with the OWASP LLM Top 10 Risks & Mitigations, ensuring coverage of the most critical and relevant threats affecting LLMs and generative AI applications.
We tailor each engagement based on how your AI is deployed, whether as a chatbot, internal AI assistant, API-driven service, or embedded within business workflows.
Our Core Focus Areas
Prompt injection and indirect prompt manipulation
Sensitive information disclosure
Data and Model Poisoning
Insecure Plugins, Extensions, and Integrations
Supply Chain and Dependency Risks
Model Misuse, Abuse, and Unauthorized Access
“We combine automation with expert-driven testing and uncover both obvious vulnerabilities and subtle AI logic flaws through AI Pentesting.”
Our Approach to AI Pentesting
Benefits of AI Pentesting
Prevents unauthorized access to AI systems
Protects sensitive and regulated data
Reduces AI misuse and abuse risks
Maintains the reliability and integrity of AI outputs
Strengthens compliance with AI security standards
Builds trust with customers, regulators, and stakeholders
Who Needs AI Pentesting?
If AI is influencing decisions, handling data, or interacting with users, it needs to be tested like a real attack surface.
Large Language Models (LLMs)
Generative AI Applications
AI Chatbots and Virtual Assistants
AI-Driven Customer Support Systems
AI APIs and Integrations
Internal AI Decision-Support Tools
FAQs
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