Run the SAME instruction over MANY items in parallel on the ParalleliX network. Ideal for bulk classify / extract / summarize / translate where each item is independent. Returns one result per item with a Proof-of-Execution hash. Use this instead of looping single calls: it fans out across the ne...
AI agents invoke parallel_map to trigger actions in Parallelix. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
This tool executes user-provided instructions on a remote distributed network. While the operations themselves (classify, extract, summarize) are typically read-only transformations, the tool's core function is to execute arbitrary instructions at scale on external infrastructure.
From the tool's definition Tool description states it 'Run[s] the SAME instruction over MANY items in parallel' and 'fans out across the network'.
Attacks that exploit this kind of access
Run the SAME instruction over MANY items in parallel on the ParalleliX network. Ideal for bulk classify / extract / summarize / translate where each item is independent. Returns one result per item with a Proof-of-Execution hash. Use this instead of looping single calls: it fans out across the network. It is categorised as a Execute tool in the Parallelix MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Parallelix MCP server in PolicyLayer and add a rule for parallel_map: 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 Parallelix. Nothing to install.
parallel_map is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the parallel_map 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 parallel_map. 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.
parallel_map is provided by the Parallelix MCP server (parallelixnetwork/parallelix-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
Teams ship this data inside their own products. See what a licence covers →