The deployment roadmap for the GPT-5.2 series has been clarified with the release of the dedicated System Card for the highly anticipated code-generation variant, GPT-5.2-Codex. Given the heightened risks associated with models capable of producing, executing, or suggesting executable code and interacting with real-world environments, this addendum mandates a significantly more stringent safety framework than its base language model counterpart. The document explicitly outlines a dual-layer safety architecture designed to mitigate the specific risks inherent in deploying powerful, task-oriented autonomous agents, shifting the focus from general content moderation to operational security and integrity.
At the core model level, the Card details specialized fine-tuning designed to handle advanced adversarial instruction sets. This includes targeted safety training aimed at frustrating complex prompt injections that attempt to coerce the model into generating malicious code, revealing proprietary training data, or circumventing internal guardrails related to harmful task execution (e.g., cyber offensive actions). This foundational layer establishes a crucial 'safety tax' baked into the LLM weights, ensuring that the model maintains a default bias towards non-harmful and compliant outputs, irrespective of external deployment constraints.
Crucially for enterprise adoption, the product-level mitigations address operational security when Codex is deployed as an autonomous agent. These measures include mandatory agent sandboxing, which isolates the model's execution environment from sensitive corporate infrastructure, and the implementation of configurable network access controls (ACLs). These external governance tools allow developers and system administrators to precisely define the model's permissible interaction vectors, controlling external API calls and resource utilization, thus providing robust risk management capabilities against potential escape sequences or unauthorized data egress.