이미지의 외곽선만 추출하여 벡터로 변환합니다.
AI agents invoke trace_outline to trigger actions in SVG Canvas MCP. 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 performs an image processing operation (outline/edge extraction) and converts raster data to vector format. This is an Execute-level operation as it runs a transformation algorithm on input data to produce derived output. It's not purely a read (it creates new vector data), not a write in the typical sense (it transforms/processes), and not destructive.
From the tool's definition "이미지의 외곽선만 추출하여 벡터로 변환합니다" — extracts outlines from an image and converts them to vector format
Attacks that exploit this kind of access
이미지의 외곽선만 추출하여 벡터로 변환합니다. It is categorised as a Execute tool in the SVG Canvas MCP MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the SVG Canvas MCP server in PolicyLayer and add a rule for trace_outline: 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 SVG Canvas MCP. Nothing to install.
trace_outline 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 trace_outline 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 trace_outline. 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.
trace_outline is provided by the SVG Canvas MCP server (kim62210/svg-canvas-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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