AI agents use memory_store to create or update resources in Ruflo — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Ruflo environment.
This tool writes/creates data by storing information into a semantic memory system. It is reversible in principle (data can be deleted or overwritten), so Write is appropriate rather than Destructive. Misuse could allow an AI agent to implant false or malicious memories into the swarm's shared knowledge base, giving it medium severity.
From the tool's definition "Store information in semantic memory"
Attacks that exploit this kind of access
Store information in semantic memory. It is categorised as a Write tool in the Ruflo MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Ruflo MCP server in PolicyLayer and add a rule for memory_store: 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 Ruflo. Nothing to install.
memory_store 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 memory_store 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 memory_store. 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.
memory_store is provided by the Ruflo MCP server (ruvnet/ruflo). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
memory_store is one line of Ruflo's registry record.
The record carries the whole server: verified identity, auth posture, risk grade, every tool classified, recommended policy — re-checked continuously.
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