AI Penetration Testing
What is AI Penetration Testing?
AI Penetration Testing, also known as LLM (Large Language Model) or ML (Machine Learning) Penetration Testing, involves scrutinising your AI setup for any potential weak spots or routes for attack. This could be for a newly integrated chatbot on your website or any other feature involving AI or LLMs. With our expertise in AI Penetration Testing, we can help guarantee that your new features don’t become new avenues for cyber attacks!
Benefits of an AI Penetration Test
As Artificial Intelligence (AI) continues to evolve and become an integral part of various business sectors, the security of these AI systems becomes increasingly crucial. From automating tasks to making data-driven decisions, AI is transforming the way businesses operate. However, with this technological advancement comes the risk of cyber threats.
An AI Penetration Test is a proactive approach to safeguard these systems. This process involves simulating cyber attacks on your AI systems to identify potential vulnerabilities. It’s akin to a stress test for your AI’s security measures, exposing any weak points that could be exploited by malicious actors.
By conducting an AI Penetration Test, you can uncover and address these vulnerabilities, thereby fortifying your AI systems. This not only ensures the security of your AI but also protects the valuable data it handles.
Common AI Attack Vectors
Training Data Poisoning This attack involves tampering with the training set used by an AI model, causing it to generate incorrect outputs, such as biases or false information. Poisoning attacks typically target AI models that use user data in their training sets.
Prompt Injection Attacks In this type of attack, a malicious actor manipulates a large-language model (LLM) that uses prompt-based learning. They strategically input prompts to make the model carry out harmful actions.
Weaponised Models In these attacks, malicious actors create files in a data format used for model exchange, like KERAS. These files often contain executable, harmful code designed to activate at a specific point and interact with a target machine or environment.
Evasion Attacks These attacks deceive machine learning (ML) models by modifying the system’s input. Rather than meddling with the AI, the attacks alter incoming data to intentionally cause a system error or bypass security measures. For instance, changing the look of a stop sign could theoretically trick a self-driving car’s AI algorithm to misinterpret the sign.
Model Denial of Service Attacks Also known as sponge attacks, these are a form of distributed denial-of-service (DDoS) attack. Similar to RegexDOS, the attacker crafts a prompt for an AI system that requests an impossible or enormous query, exhausting the system’s resources and increasing costs for the model owner.
Data Privacy Attacks Sometimes, ML models use real user interactions as training data. If users share confidential information with the AI during these interactions, they risk their organisation’s security. Attackers could theoretically access this sensitive data by inputting the right series of queries.
Model Theft Attackers may attempt to steal proprietary AI models through traditional methods, like phishing or password attacks on private source code repositories. Symbolic AI is particularly vulnerable to model theft as it’s an ‘expert system’ with fixed queries and responses. To exploit Symbolic AI, attackers simply record all possible answers to each question and navigate the ‘answer tree’.
Furthermore, a 2016 study by Cornell Tech showed that it’s possible to reverse engineer models through systematic queries, making them susceptible to model theft