Developing AI in the cloud also exposes organizations to new attack vectors and risk types that traditional security tools may not be equipped to detect or mitigate. Some examples include:
Prompt Injection (LLM01): Imagine a large language model used for generating marketing copy. An attacker could craft a special prompt that tricks the model into generating harmful or offensive content, damaging the company's brand and reputation.
Training Data Poisoning (LLM03, ML02): Adversaries can tamper with training data to compromise the integrity and reliability of cloud-based AI models. In the case of an AI model used for image recognition in a security surveillance system, poisoned training data containing mislabeled images could cause the model to generate incorrect classifications, potentially missing critical threats.
Model Theft (LLM10, ML05): Unauthorized access to proprietary AI models deployed in the cloud poses risks to intellectual property and competitive advantage. If a competitor were to steal a model trained on a company's sensitive data, they could potentially replicate its functionality and gain valuable insights.
Supply Chain Vulnerabilities (LLM05, ML06): Compromised libraries, datasets, or services used in cloud AI development pipelines can lead to widespread security breaches. A malicious actor might introduce a vulnerability into a widely used open-source library for AI, which could then be exploited to gain access to AI models deployed by multiple organizations.