Chinese large-model vendor Zhipu has spent the past week scrambling to repair a privacy-trust crisis triggered by a single individual developer.
On September 18, blogger ferstar posted on a developer forum that Zhipu's AI coding assistant ZCode was encrypting and uploading the user's full local workspace to Aliyun OSS whenever they remained signed in, with no way to disable the behavior in the client. A 313 MB encrypted archive only stayed on disk because the upload failed 564 times in a row. The archive contained the entire core asset of one of his commercial projects. Word spread quickly in the developer circle, and enterprise customers immediately asked Zhipu to clarify whether any of the uploaded data had been transferred across borders or stored overseas.
From "we apologize" to "match overseas head-vendor rules"
On the evening of September 20, Zhipu announced on its official WeChat account that its Model as a Service (MaaS) platform would soon roll out a "data-content non-retention" feature that enterprises and developers can opt into through the MaaS console. Once activated, user prompts and model outputs are used only inside the single inference call and are not persisted afterwards, nor fed into model iteration training.
Cailianshe (CLS) characterized the move as "the most stringent privacy standard seen to date among Chinese LLM service providers", and pointed out that Anthropic, OpenAI and other overseas head-vendors have long offered similar zero-retention options in their enterprise APIs. Zhipu is essentially backfilling the head-vendor rule at the MaaS entry point in China.
The two explicitly carved-out exceptions
Zero retention is not "stateless in every scenario." Zhipu spelled out two classes of exceptions. The first is product-form-driven: Batch API and Files API must persist jobs and files by design and fall outside zero-retention coverage. The second is compliance-driven: to satisfy Chinese legal and regulatory requirements, or to investigate suspected violations or service abuse, the platform may retain the relevant data for 30 days or longer.
This "default off, exception on, with an audit trail" boundary matches the way OpenAI and Anthropic handle zero-retention in their enterprise APIs.
Open-sourcing ZCode in full as restitution
To close the trust gap, Zhipu did three things. First, it formally open-sourced ZCode on September 21 and handed the codebase over to community supervision. Second, it invited the China Academy of Information and Communications Technology (CAICT) and NSFOCUS to run a third-party security audit on ZCode, with the audit's focus on data retention behavior. Third, it confirmed that the zcode-prod Aliyun OSS bucket is in a zero-data state, that the ZCode v3.14.0 client has removed the Repo Wiki feature and severed the local repo snapshot generation and upload chain, and that a permanent product security vulnerability reporting mechanism has been put in place.
Zhipu said ZCode was built so China could have an AI coding tool on par with Claude Code, and that open-sourcing the project is a chance for the community to explore how Harness-style techniques can further unlock model capability. Open-sourcing a product under active controversy is a notably unusual stance among the domestic model vendors.
Not a model-capability question, but an enterprise-trust one
The core signal here is that the next watershed in the LLM race is shifting from "whose benchmark is higher" to "whose enterprise data governance is more trustworthy." Once high-sensitivity assets like source code, contracts, and R&D notes enter the cloud, the first questions enterprise buyers ask are no longer about model accuracy. They are about whether their data was retained, whether it was used for training, and whether it can leave the country.
By porting the enterprise-grade zero-retention mechanism that OpenAI and Anthropic already treat as default into the Chinese MaaS stack, and by responding to the silent-upload incident with an open-source plus third-party-audit posture, Zhipu is effectively pushing the AI coding-tool competition from "can the model write code" to "will enterprises trust it with their code". For other domestic LLM vendors, MaaS-side zero retention will quickly become the entry-level bar for enterprise customers, not a differentiator.