AI agents use block_add_attackers to create or update resources in AF_MCP — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your AF_MCP environment.
An AI agent can call block_add_attackers faster than any human can review — one bad instruction and it creates or modifies resources in AF_MCP by the hundred, each call as confident as the last.
Attacks that exploit this kind of access
批量新增封锁攻击者,并在执行前进行白名单和人工确认校验。. It is categorised as a Write tool in the AF_MCP MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the AF_ MCP server in PolicyLayer and add a rule for block_add_attackers: 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 AF_MCP. Nothing to install.
block_add_attackers 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 block_add_attackers 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 block_add_attackers. 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.
block_add_attackers is provided by the AF_ MCP server (xiaqijun/af_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.
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