Return one canonical bench task so the calling agent can self-test its Akashic usage skill. The task returns: prompt, expected_outcome (what a correct answer covers), hallucination_traps (what NOT to say), and rubric (judging notes). The agent then answers the prompt using its normal tool usage, ...
Risk signalsAdmin/system-level operation
Part of the OpenAkashic server.
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AI agents invoke run_self_test to trigger processes or run actions in OpenAkashic. Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.
run_self_test can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. PolicyLayer enforces rate limits and validates arguments to keep execution within safe bounds.
Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.
{
"version": "1",
"default": "deny",
"tools": {
"run_self_test": {
"limits": [
{
"counter": "run_self_test_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}See the full OpenAkashic policy for all 35 tools.
These attack patterns abuse exactly the kind of access run_self_test gives an agent. Each links to the full case and the policy that stops it:
Other execute tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Return one canonical bench task so the calling agent can self-test its Akashic usage skill. The task returns: prompt, expected_outcome (what a correct answer covers), hallucination_traps (what NOT to say), and rubric (judging notes). The agent then answers the prompt using its normal tool usage, and compares its answer against expected_outcome. This is self-assessment — no server-side judgment happens here. The judge script at closed-web/server/bench/judge.py can be run manually by an admin to score actual responses.. It is categorised as a Execute tool in the OpenAkashic MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the OpenAkashic MCP server in PolicyLayer and add a rule for run_self_test: 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 OpenAkashic. Nothing to install.
run_self_test 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 run_self_test 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 run_self_test. 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.
run_self_test is provided by the OpenAkashic MCP server (https://knowledge.openakashic.com/mcp/). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 35 OpenAkashic tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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