set_stop_loss
AI agents use set_stop_loss to create or update resources in HyperLiquid MCP Server — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your HyperLiquid MCP Server environment.
An AI agent can call set_stop_loss faster than any human can review — one bad instruction and it creates or modifies resources in HyperLiquid MCP Server by the hundred, each call as confident as the last.
Attacks that exploit this kind of access
set_stop_loss. It is categorised as a Write tool in the HyperLiquid MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the HyperLiquid MCP Server MCP server in PolicyLayer and add a rule for set_stop_loss: 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 HyperLiquid MCP Server. Nothing to install.
set_stop_loss 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 set_stop_loss 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 set_stop_loss. 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.
set_stop_loss is provided by the HyperLiquid MCP Server MCP server (talkincode/hyperliquid-mcp-python). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.