mlsanityData to 7 Oct 2026

Are You Still the Agent I Authorized? Earned Authority under a Fixed Ceiling for Evolving Agents

arXiv 2607.23586 ↗First posted 26 Jul 2026cs.AI cs.CRcited by 4

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Abstract (arXiv)

Long-lived AI agents increasingly evolve after deployment by retaining experience, acquiring skills and tools, revising workflows, delegating work, and moving across task phases. This improves adaptation but creates a distinct authorization problem. Tool-enabled agents can turn model errors and prompt injections into consequential external actions; when evolution occurs under a live grant, the subject exercising that authority or the context in which it acts may no longer match what the user evaluated. Evolution can change both the effects reachable under an old grant and the authority required by the task, which may rise, fall, or become incomparable. Existing tool policies constrain actions but do not determine when a grant survives this change. We formulate authorization continuity: when does an existing grant remain valid, how may active authority change, and what boundary must never move? Our state-bound model fixes a transition envelope and an immutable effect ceiling at grant time. The envelope determines whether the grant survives a mutation; below the ceiling, authority may contract freely and expand only under specified evidence conditions. We distinguish requested from realized effects and prove that, under complete mediation, sound effect abstraction, attenuating delegation, and monitor integrity, mutation cannot amplify protected effects beyond the user-issued ceiling. Agent-produced evidence may allocate authority below the ceiling but cannot raise it. Finally, we map six mutation classes to their authorization consequences.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 28 of its 43 references that are in the map. First posted on arXiv 26 Jul 2026.

Builds on

28 of 43 references.

References · 28

  1. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  2. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
  3. 2023ExpeL: LLM Agents Are Experiential Learnerscited by 814
  4. 2024Automated Design of Agentic Systemscited by 360
  5. 2025Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agentscited by 294
  6. 2025Defeating Prompt Injections by Designcited by 256
  7. 2025A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligencecited by 155
  8. 2025Progent: Securing AI Agents with Privilege Controlcited by 121
  9. 2025Lifelong Learning of Large Language Model based Agents: A Roadmapcited by 78
  10. 2025Design Patterns for Securing LLM Agents against Prompt Injectionscited by 67
  11. 2025Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agentscited by 66
  12. 2025Your Agent May Misevolve: Emergent Risks in Self-evolving LLM Agentscited by 52
16 more
  1. 2024G"odel Agent: A Self-Referential Agent Framework for Recursive Self-Improvementcited by 27
  2. 2026From Untrusted Input to Trusted Memory: A Systematic Study of Memory Poisoning Attacks in LLM Agentscited by 23
  3. 2026On Safety Risks in Experience-Driven Self-Evolving Agentscited by 12
  4. 2026OEP: Poisoning Self-Evolving LLM Agents via Locally Correct but Non-Transferable Experiencescited by 9
  5. 2021Self-Supervised Adversarial Example Detection by Disentangled Representationcited by 7
  6. 2022Evaluating Membership Inference Through Adversarial Robustnesscited by 7
  7. 2025Character-Level Perturbations Disrupt LLM Watermarkscited by 7
  8. 2026Governing Dynamic Capabilities: Cryptographic Binding and Reproducibility Verification for AI Agent Tool Usecited by 7
  9. 2026Safety in Self-Evolving LLM Agent Systems: Threats, Amplification, and Case Studiescited by 6
  10. 2026Dynamic Malicious Skills in Agentic AIcited by 5
  11. 2026Deontic Policies for Runtime Governance of Agentic AI Systemscited by 5
  12. 2025Exploring Gradient-Guided Masked Language Model to Detect Textual Adversarial Attackscited by 4
  13. 2026Less Is More -- Until It Breaks: Security Pitfalls of Vision Token Compression in Large Vision-Language Modelscited by 4
  14. 2026Options, Not Clicks: Lattice Refinement for Consent-Driven MCP Authorizationcited by 4
  15. 2026SkillGuard: A Permission-Centric Framework for Agent Skill Securitycited by 4
  16. 2026Language-Based Agent Controlcited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.