# The Authorization-Execution Gap Is a Major Safety and Security Problem in Open-World Agents

arXiv: https://arxiv.org/abs/2605.11003 · HTML: https://mlsanity.com/paper/2605.11003/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 36 references that are in the map.
- First posted on arXiv 10 May 2026.

## Builds on (most cited first, 27 of 27)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [WebArena: A Realistic Web Environment for Building Autonomous Agents](https://mlsanity.com/paper/2307.13854/) (2023)
- [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://mlsanity.com/paper/2302.12173/) (2023)
- [OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments](https://mlsanity.com/paper/2404.07972/) (2024)
- [GAIA: a benchmark for General AI Assistants](https://mlsanity.com/paper/2311.12983/) (2023)
- [Identifying the Risks of LM Agents with an LM-Emulated Sandbox](https://mlsanity.com/paper/2309.15817/) (2023)
- [InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents](https://mlsanity.com/paper/2403.02691/) (2024)
- [AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents](https://mlsanity.com/paper/2410.09024/) (2024)
- [AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases](https://mlsanity.com/paper/2407.12784/) (2024)
- [Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents](https://mlsanity.com/paper/2410.02644/) (2024)
- [AssistantBench: Can Web Agents Solve Realistic and Time-Consuming Tasks?](https://mlsanity.com/paper/2407.15711/) (2024)
- [Memory Injection Attacks on LLM Agents via Query-Only Interaction](https://mlsanity.com/paper/2503.03704/) (2025)
- [SafeArena: Evaluating the Safety of Autonomous Web Agents](https://mlsanity.com/paper/2503.04957/) (2025)
- [PentestGPT: An LLM-empowered Automatic Penetration Testing Tool](https://mlsanity.com/paper/2308.06782/) (2023)
- [AgentDAM: Privacy Leakage Evaluation for Autonomous Web Agents](https://mlsanity.com/paper/2503.09780/) (2025)
- [Zombie Agents: Persistent Control of Self-Evolving LLM Agents via Self-Reinforcing Injections](https://mlsanity.com/paper/2602.15654/) (2026)
- [Agents of Chaos](https://mlsanity.com/paper/2602.20021/) (2026)
- [OS-Kairos: Adaptive Interaction for MLLM-Powered GUI Agents](https://mlsanity.com/paper/2503.16465/) (2025)
- [Rethinking the Reliability of Multi-agent System: A Perspective from Byzantine Fault Tolerance](https://mlsanity.com/paper/2511.10400/) (2025)
- [When Benign Inputs Lead to Severe Harms: Eliciting Unsafe Unintended Behaviors of Computer-Use Agents](https://mlsanity.com/paper/2602.08235/) (2026)
- [CI-Work: Benchmarking Contextual Integrity in Enterprise LLM Agents](https://mlsanity.com/paper/2604.21308/) (2026)
- [A Safety and Security Framework for Real-World Agentic Systems](https://mlsanity.com/paper/2511.21990/) (2025)
- [AgentSCOPE: Evaluating Contextual Privacy Across Agentic Workflows](https://mlsanity.com/paper/2603.04902/) (2026)
- [Helpful Agent Meets Deceptive Judge: Understanding Vulnerabilities in Agentic Workflows](https://mlsanity.com/paper/2506.03332/) (2025)
- [Formalizing the Safety, Security, and Functional Properties of Agentic AI Systems](https://mlsanity.com/paper/2510.14133/) (2025)
- [Securing Agentic AI Systems -- A Multilayer Security Framework](https://mlsanity.com/paper/2512.18043/) (2025)

## Built on by (most cited first, 4 of 4)

- [Authorization Revocation for Long-Running AI Agents: Root-Scoped Quiescence under Delegation and Asynchronous Execution](https://mlsanity.com/paper/2609.21284/) (2026)
- [The Vulnerability With No CVE: Managing Persistent Gaps Between Mandate and Authority in AI Coding Agents](https://mlsanity.com/paper/2608.05884/) (2026)
- [DelegationBench: Measuring When AI Agents Should Ask Before Acting](https://mlsanity.com/paper/2610.05532/) (2026)
- [APEX-Harm: Evaluating Agent Safety in Shared Professional Workspaces](https://mlsanity.com/paper/2610.13623/) (2026)

## Abstract (arXiv)

This position paper argues that the Authorization-Execution Gap (AEG) is a major safety and security problem in open-world agents. The AEG is the divergence between what a principal intends to authorize and what an open-world agent ultimately executes. Because such agents act autonomously across tools, persistent state, and multi-agent handoffs, even small instances of authorization divergence can cause harm that is difficult or impossible to undo. We argue that many observed agent failures can be traced to three structural sources of AEG: delegation-level incompleteness, channel-level corruption, and composition-level fragmentation. The same observed failure may arise from any of these sources. Without identifying the source, a defense targeting the symptom alone cannot address the underlying cause. Agent safety and security should therefore emphasize source-oriented diagnosis and defense. Because the structural sources of AEG arise dynamically during execution, this approach necessarily requires authorization integrity checks applied during execution, rather than relying solely on one-shot upfront filtering or post-hoc audit. For NeurIPS, the implication is that papers on open-world agents should report not only outcome-level metrics such as task success or attack resistance, but also process-level evidence showing where AEG was detected, constrained, and attributed to a structural source during execution.