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Apply least-privilege tool allowlisting, identity binding, and human-in-the-loop controls for agent tool calls.
Passed 20 of 22 test cases with the skill, and 14 of 22 without it.
and 5 more in the full report
6 fixed14 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
How should a Python authorization gate output decision event logs so they are instantly formatted for SIEM ingestion?
| Checked | Without skill | With skill |
|---|---|---|
| The event dictionary is serialized to a JSON string using json.dumps. | Missed | Passed |
| The serialized event is printed or emitted immediately after decision generation. | Passed | Passed |
The instructions your model reads when the skill loads.
Authorized-use-only notice: This is a defensive skill. The controls below govern how an AI agent invokes tools/plugins. Deploy them on systems you own or operate. Test guardrail bypasses only against your own agent in a non-production environment.
Autonomous (agentic) AI systems decide which tool to call, with what arguments, and when, based on model reasoning over untrusted inputs. That makes the tool-invocation boundary the highest-risk control point in an agent: a single successful prompt injection or a poisoned tool can turn the agent into a confused deputy that deletes data, sends money, or pivots into connected systems. The relevant threat is MITRE ATLAS AML.T0053 (LLM Plugin Compromise) and the OWASP Agentic AI Top 10 classes for Tool Misuse, Excessive Agency, and Privilege Compromise.
The defense is layered, defense-in-depth governance of tool calls: (1) a strict allowlist of which tools the agent may call and with which argument shapes; (2) least-privilege identity binding so each tool call runs with scoped, short-lived credentials tied to the acting user/session — not a single god-mode service account; (3) policy enforcement at the call boundary (NVIDIA NeMo Guardrails dialog/flow rails and tool guardrails, or a deterministic policy wrapper); (4) human-in-the-loop (HITL) approval for high-impact actions; and (5) audit logging of every invocation for detection. This skill implements all five with verified, runnable patterns using NeMo Guardrails and a framework-agnostic Python policy wrapper.
python -m venv .venv && source .venv/bin/activate
# NVIDIA NeMo Guardrails — programmable rails incl. tool/flow controls
pip install nemoguardrails
# JSON schema validation for tool argument allowlisting
pip install jsonschema
# (Optional) cloud SDK for scoped credential issuance, e.g. AWS STS
pip install boto3
test cases passed, vs 14 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.