Top 14 AI Security Risks in 2026

SentinelOne catalogs major AI security risks such as data poisoning, model inversion, adversarial examples, privacy leakage, backdoor attacks, model stealing, evasion attacks, and API exploitation.[6] It highlights how attackers can retrieve sensitive text content from models, manipulate outputs via crafted inputs, and exploit insecure endpoints, and recommends mitigations like strong data validation, model encryption, multi-factor authentication, and differential privacy frameworks.[6]

According to SentinelOne, major AI security risks for 2026 include data poisoning, model inversion, adversarial examples, privacy leakage, backdoor attacks, model stealing, evasion attacks, and API exploitation.[2] The report explains how attackers can retrieve sensitive text content from models, manipulate outputs via crafted inputs, and exploit insecure endpoints, and recommends mitigations such as strong data validation, model encryption, multi-factor authentication, and differential privacy.[2] From a CyberSE.AI perspective, model inversion and related inference attacks represent a critical data leakage vector, so organizations should prioritize AI Security Readiness Assessments to map where sensitive training data can be inferred, and AI Agent Business Logic Audits to identify unsafe query patterns, over-permissive APIs, and missing access controls around model outputs.