webgpu_memory_layout
Analyze GPU memory allocations and buffer usage. Identifies memory layout patterns that may be vulnerable to side-channel attacks. With track=true, snapshots are stored on the shared state board (webgpu_memory_<canvasId>) and the response includes the delta vs the previous snapshot plus the growt...
This record as markdown: /tools/io-github-vmoranv-jshookmcp/webgpu-memory-layout.md
What webgpu_memory_layout does on Jshookmcp
AI agents call webgpu_memory_layout to retrieve information from Jshookmcp without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
Why webgpu_memory_layout is rated Low
Tool analyzes memory patterns for vulnerability detection without modifying or executing code, but reveals sensitive architectural information.
From the tool's definition Analyze GPU memory allocations and buffer usage; identifies patterns vulnerable to side-channel attacks.
Attacks that exploit this kind of access
The rule that runs webgpu_memory_layout safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Jshookmcp, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For webgpu_memory_layout, this is the rule to start with:
webgpu_memory_layout is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Jshookmcp, apply this rule, and every webgpu_memory_layout call is checked against it from then on.
Questions about webgpu_memory_layout
Analyze GPU memory allocations and buffer usage. Identifies memory layout patterns that may be vulnerable to side-channel attacks. With track=true, snapshots are stored on the shared state board (webgpu_memory_<canvasId>) and the response includes the delta vs the previous snapshot plus the growth rate in KB/s. It is categorised as a Read tool in the Jshookmcp MCP Server, which means it retrieves data without modifying state.
Register the Jshook MCP server in PolicyLayer and add a rule for webgpu_memory_layout: 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 Jshookmcp. Nothing to install.
webgpu_memory_layout is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the webgpu_memory_layout 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 webgpu_memory_layout. 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.
webgpu_memory_layout is provided by the Jshook MCP server (@jshookmcp/jshook). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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