OpenAI CISO: mitigation of prompt injection risks in Atlas (twitter.com)

OpenAI’s Chief Information Security Officer said the company is actively addressing prompt-injection risks in Atlas, OpenAI’s system for connecting models to external data and tools. The CISO framed prompt injection as a primary threat vector — attackers can manipulate user or retrieved content to make models reveal secrets, execute unintended actions, or misuse tool integrations — and described a layered defense approach to reduce that risk across ingestion, retrieval, and execution stages. Technically, the mitigation strategy combines input sanitization and provenance controls, retrieval filtering and policy-based scoring, model-side classifiers to detect malicious prompts, strict capability-based access for external tools, and sandboxing with audit logging for any action the model requests. OpenAI emphasized continuous adversarial testing (red-teaming), real-time monitoring, and fine-grained permissioning so that high-risk operations require explicit authorization or human review. For the AI/ML community, these controls highlight practical patterns — provenance tagging, least-privilege tool access, model-based detection, and auditable workflows — that teams should adopt when exposing LLMs to external data or tools to prevent injection attacks and preserve safety at scale.