5 Generative AI Security Threats You Must Know About

This document, prepared by Microsoft, offers a comprehensive guide on the security risks introduced by generative AI and how organizations can effectively protect themselves. While generative AI accelerates threat detection and automates repetitive tasks, it also expands the threat landscape and empowers attackers with more sophisticated tactics, especially in cloud environments.

The document is aimed at security teams, IT leaders, and business executives who are implementing or planning to integrate generative AI applications into their organizations. According to data presented, 95% of security and IT decision-makers are planning or actively developing generative AI technology, and 66% of organizations are developing custom generative AI applications.

The guide identifies three fundamental security challenges: first, most generative AI applications are cloud-based, making it easier for attackers to exploit vulnerabilities to move laterally and compromise sensitive data; second, AI models require access to large datasets, making them attractive targets with data leakage risks; and third, AI model outputs are variable and difficult to predict, complicating control of model behavior.

The core of the document presents the 5 main generative AI threats based on OWASP and MITRE ATLAS: poisoning attacks that manipulate training data, evasion attacks that circumvent security systems, functional extraction where adversaries recreate models through repeated queries, inversion attacks that infer information about model parameters, and prompt injection attacks that manipulate the model toward unintended behaviors.

As a solution, the document proposes adopting cloud-native application protection platforms (CNAPP) that unify multiple security solutions from development to runtime. Specifically, it presents Microsoft Defender for Cloud as a comprehensive solution that combines AI security posture management (AI-SPM) with real-time threat protection, backed by over 84 trillion daily signals from Microsoft Threat Intelligence. It includes success stories from companies like Icertis and Mia Labs that have implemented these solutions to protect their generative AI applications in production environments.

Key points

• Generative AI accelerates threat detection but also expands the risk landscape for attackers
• 95% of security and IT decision-makers are integrating or developing generative AI technology
• Three main challenges: cloud vulnerabilities, massive data exposure, and unpredictable model behavior
• Five critical threats: poisoning, evasion, functional extraction, inversion, and prompt injection
• 80% of business leaders consider AI data leakage as a top concern
• 88% of organizations worried about indirect prompt injection attacks
• CNAPP platforms unify security from development to runtime in multicloud environments
• Microsoft Defender for Cloud offers AI-SPM posture management and real-time protection
• Defender leverages over 84 trillion daily threat intelligence signals from Microsoft