mlsanityData to 14 Oct 2026

The Verifiable Action Card: Trustworthy Human-in-the-Loop Control for Secure Autonomous Agents

arXiv 2609.18411 ↗First posted 16 Sep 2026cs.CR cs.SY eess.SPcited by 2

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

also in 1 more
Abstract (arXiv)

Agentic browsers can execute security-sensitive actions under a user's authenticated session, making indirect prompt injection and deceptive confirmation interfaces a direct threat to action integrity. Existing human-in-the-loop (HITL) safeguards are insufficient when the approval prompt itself can be influenced by untrusted page content or model-generated text. We present the Verifiable Action Card (VAC), an architectural defence that reconstructs approval information from the ground-truth pending browser action and trusted intent provenance, renders it out-of-band in the trusted browser chrome, and binds approval to the exact action re-verified at dispatch. VAC combines provenance fencing, a ground-truth action descriptor, default-deny confirmation, provenance-aware risk gating, and execution binding. We implement VAC in a complete agentic browser and evaluate it on a 24-scenario benchmark covering confused-deputy attacks, Lies-in-the-Loop dialog forging, indirect prompt injection, adaptive action substitution, provenance evasion, and legitimate tasks. Across the evaluated LLMs, attack success without VAC ranges from 68% to 100%, whereas VAC reduces attack success to 0% on every model, with 78% legitimate-task completion and a 0% false-block rate. These results show that grounding approval in the action that will actually execute provides architectural protection against security failures that prompt-level defences and conventional HITL confirmation cannot reliably prevent.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 18 of its 33 references that are in the map. First posted on arXiv 16 Sep 2026.

Builds on

18 of 33 references.

Closest ancestors · 3

Same direction, newest first

  1. 2026WAAA! Web Adversaries Against Agentic Browsers6 May
  2. 2025Mind the Web: The Security of Web Use Agents8 Jun
  3. 2024InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents5 Mar

Other references · 15

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 2,006
  3. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,443
  4. 2023Mind2Web: Towards a Generalist Agent for the Webcited by 1,242
  5. 2022Ignore Previous Prompt: Attack Techniques For Language Modelscited by 832
  6. 2024GPT-4V(ision) is a Generalist Web Agent, if Groundedcited by 563
  7. 2024The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructionscited by 493
  8. 2024WebVoyager: Building an End-to-End Web Agent with Large Multimodal Modelscited by 438
  9. 2024StruQ: Defending Against Prompt Injection with Structured Queriescited by 375
  10. 2018Reinforcement Learning on Web Interfaces Using Workflow-Guided Explorationcited by 284
  11. 2024Defending Against Indirect Prompt Injection Attacks With Spotlightingcited by 267
  12. 2024Agent-E: From Autonomous Web Navigation to Foundational Design Principles in Agentic Systemscited by 81
3 more
  1. 2025Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judgecited by 76
  2. 2025Agentic Web: Weaving the Next Web with AI Agentscited by 45
  3. 2026What You Approve Is What Executes: Consent Integrity for Black-Box LLM Agentscited by 9

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026Real-Time Trust Verification for Safe Agentic Actions using TrustBench
  2. 2025Fortifying the Agentic Web: A Unified Zero-Trust Architecture Against Logic-layer Threats
  3. 2026HDP: A Lightweight Cryptographic Protocol for Human Delegation Provenance in Agentic AI Systems
  4. 2026CAVA: Canonical Action Verification and Attestation for Runtime Governance of Agentic AI Systems
  5. 2026PACE: Provenance-Aware Capability Enforcement for Tool-Using LLM Agents
  6. 2025SAFEFLOW: A Principled Protocol for Trustworthy and Transactional Autonomous Agent Systems
  7. 2026Towards Safe Autonomy for Computer Use
  8. 2026Prismata: Confining Cross-Site Prompt Injection in Web Agents