Empirical Evaluation of Task-Based Permission Scoping Architecture for AI Agents
cs.AI, cs.CR
Submitted: 2026-09-14
Updated: 2026-09-14
Code: https://github.com/0xballistics/mostargate
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Alignment faking in large language models
- Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training
- Cloud Programming Simplified: A Berkeley View on Serverless Computing
- Frontier Models are Capable of In-context Scheming
- Model Organisms for Emergent Misalignment
- A Systematic Security Evaluation of OpenClaw and Its Variants
Related papers
- MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
- Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
- The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
- MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
- Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems
- VSAL: A Vision Solver with Adaptive Layouts for Graph Property Detection