This record as markdown: /tools/aws/update-resource.md
What update_resource does on AWS
AI agents use update_resource to create or update resources in AWS, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your AWS environment.
Why update_resource is rated Medium
The name 'update_resource' strongly suggests a reversible modification operation (Write category). Without a description, confidence is reduced but remains moderately high. In AWS contexts, update operations on resources can have significant blast radius if misapplied (e.g., updating security group rules, IAM resources, or database configs), justifying high severity.
From the tool's definition Tool name 'update_resource' indicates modification of data/resources. Description is empty, preventing full certainty.
Attacks that exploit this kind of access
The rule that runs update_resource safely
PolicyLayer is an MCP gateway: it sits between your AI agents and AWS, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For update_resource, this is the rule to start with:
update_resource stays usable, but capped: an agent stuck in a loop can't make hundreds of changes a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect AWS, apply this rule, and every update_resource call is checked against it from then on.
Questions about update_resource
update_resource is a write tool on the AWS MCP server. It is categorised as a Write tool in the AWS MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the AWS MCP server in PolicyLayer and add a rule for update_resource: 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. Nothing to install.
update_resource 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 update_resource 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 update_resource. 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.
update_resource is provided by the AWS MCP server (@awslabs/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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