mlsanityData to 7 Oct 2026

Capability Gates Are Not Authorization: Confused-Deputy Failures in LLM Agent Frameworks

arXiv 2606.28679 ↗First posted 27 Jun 2026cs.CR cs.AIcited by 6

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

Tool-using LLM agents increasingly read untrusted content while holding side-effecting tools such as payments, email, CRM, and infrastructure APIs, yet common framework defaults still conflate tool exposure with authorization. We audit whether LangChain/LangGraph, LlamaIndex, and the Stripe Agent Toolkit re-authorize each model-emitted call, with concrete argument values, before execution. Across pinned public-source commits, all three provide capability gating by default, but none provides a deterministic fail-closed per-call value authorization gate by default. We introduce ScopeGate, a five-stage PDP/PEP for agent tool calls: scope, authorization, money ceiling, idempotency, and default deny. Evaluation shows the identical unauthorized payout call executes under LangChain's default dispatch (with a companion LlamaIndex PoC) but is denied by ScopeGate; the tested control reports 0/48 static bypasses, 0/29 unauthorized attempts (40-iteration adaptive run), 0/10 benign false-denies, and Latam-GPT payment-agent containment at 10/10. ASR denotes attempted unauthorized action, containment is not a cure, deployment-tier claims are inference over measured model classes, and no CVE is asserted.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 5 of its 15 references that are in the map. First posted on arXiv 27 Jun 2026.

Builds on

5 of 15 references.

Closest ancestors · 4

Same direction, newest first

  1. 2026Whispers of Wealth: Red-Teaming Google's Agent Payments Protocol via Prompt Injection30 Jan
  2. 2025WASP: Benchmarking Web Agent Security Against Prompt Injection Attacks22 Apr
  3. 2024InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents5 Mar
  4. 2023Identifying the Risks of LM Agents with an LM-Emulated Sandbox25 Sep

Other references · 1

  1. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,339

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 6.

6 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026PACE: Provenance-Aware Capability Enforcement for Tool-Using LLM Agents
  2. 2025MiniScope: A Least Privilege Framework for Authorizing Tool Calling Agents
  3. 2026Securing LLM Agents Need Intent-to-Execution Integrity
  4. 2026ContractBench: Can LLM Agents Preserve Observation Contracts?
  5. 2026Guardrails as Scapegoats: Auditing Unfaithful Safety Refusals in Tool-Augmented LLM Agents
  6. 2025More Vulnerable than You Think: On the Stability of Tool-Integrated LLM Agents
  7. 2026AgentProv: Auditing Agentic LLM API Providers via Tool-use Policy Probes
  8. 2026ToolFence: Fine-Grained Authorization for Secure Tool-Using LLM Agents