AI agents use write_object to create or update resources in Storage — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Storage environment.
The tool creates or modifies data in a reversible manner (objects can be overwritten or deleted later). While it interacts with storage, it does not irreversibly destroy data (which would be Destructive), nor does it execute arbitrary code or trigger external operations (which would be Execute).
From the tool's definition The tool is named 'write_object' and the description states it 'Writes a new object to the bucket.' This explicitly indicates creation or modification of data in a GCS bucket.
Attacks that exploit this kind of access
Writes a new object to the bucket. It is categorised as a Write tool in the Storage MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Storage MCP server in PolicyLayer and add a rule for write_object: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Storage. Nothing to install.
write_object is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the write_object rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for write_object. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
write_object is provided by the Storage MCP server (@google-cloud/storage-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.