Prepend a string to an existing value.
AI agents use cache_prepend to create or update resources in AWS Labs Aurora DSQL MCP Server — usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your AWS Labs Aurora DSQL MCP Server environment.
The tool modifies existing data by prepending a string to it. This is a reversible write operation (the original value can be restored), making Write the appropriate category. Severity is medium because an AI agent could corrupt data by prepending unintended content to values, but the effect is reversible.
From the tool's definition Prepend a string to an existing value
Attacks that exploit this kind of access
Prepend a string to an existing value. It is categorised as a Write tool in the AWS Labs Aurora DSQL MCP Server MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the AWS Labs Aurora DSQL MCP Server MCP server in PolicyLayer and add a rule for cache_prepend: 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 AWS Labs Aurora DSQL MCP Server. Nothing to install.
cache_prepend 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 cache_prepend 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 cache_prepend. 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.
cache_prepend is provided by the AWS Labs Aurora DSQL MCP Server MCP server (awslabs.aurora-dsql-mcp-server). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.