Capability Minimization as a Safety Primitive: Risk-Aware Causal Gating for Least-Privilege LLM Agents
arXiv:2606.13884v1 Announce Type: new Abstract: Modern decision systems increasingly rely on learned components whose outputs may be confident yet wrong, exposing downstream actions to costly errors. We introduce Risk-Aware Causal Gating (RACG), a framework that decides whether to act on, defer, or abstain from a model's prediction by combining causal effect estimation with calibrated risk control. RACG models the causal pathway from candidate actions to outcomes and gates each decision according to an estimated counterfactual risk rather than raw predictive confidence. To make gating reliable
The accelerating deployment of LLM agents in critical decision systems necessitates robust safety mechanisms to mitigate inherent risks, making this research timely.
It introduces a novel framework for risk-aware decision-making in AI agents, moving beyond simple confidence scores to address costly errors and promote reliable autonomous systems.
The approach shifts from relying solely on predictive confidence to integrating causal effect estimation and calibrated risk control for gating AI agent actions.
- · AI safety researchers
- · Developers of autonomous systems
- · Organizations deploying AI agents
- · AI assurance providers
- · Organizations over-relying on un-gated AI
- · Models with high confidence but poor calibration
Reduced catastrophic failures from autonomous AI agents due to enhanced decision gating.
Increased trust and adoption of AI systems in high-stakes environments as reliability improves.
New regulatory frameworks for AI safety prioritizing causal reasoning and risk control in agentic deployment.
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Read at arXiv cs.AI