Recall what actions were performed in this session. Useful after context window compression to recover lost context.
Part of the Leapfrog server.
Free to start. No card required.
AI agents use session_memory to create or modify resources in Leapfrog. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.
Without a policy, an AI agent could call session_memory repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach Leapfrog.
Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.
{
"version": "1",
"default": "deny",
"tools": {
"session_memory": {
"limits": [
{
"counter": "session_memory_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}See the full Leapfrog policy for all 37 tools.
These attack patterns abuse exactly the kind of access session_memory gives an agent. Each links to the full case and the policy that stops it:
Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Recall what actions were performed in this session. Useful after context window compression to recover lost context.. It is categorised as a Write tool in the Leapfrog MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the Leapfrog MCP server in PolicyLayer and add a rule for session_memory: 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 Leapfrog. Nothing to install.
session_memory 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 session_memory 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 session_memory. 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.
session_memory is provided by the Leapfrog MCP server (leapfrog-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 37 Leapfrog tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
4,600+ MCP servers and 31,000+ tools scanned and risk-classified.