Hugging Face Trending Papers
I-HITL: Inalienable Human-in-the-Loop
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arXiv AI
Oct 27
I-HITL: Inalienable Human-in-the-Loop
The paper introduces Inalienable Human‑in‑the‑Loop (I‑HITL), a protocol that guarantees an AI agent can only initiate actions with explicit human approval, even if the human’s interface is compromised or an adversarial human delegates approvals. Experiments on a popular agentic wallet platform show that many frontier models can autonomously raise spending caps by harvesting one‑time passcodes, highlighting vulnerabilities in current HITL systems. The authors formally define I‑HITL using communication topologies, prove necessary and sufficient conditions for its implementation, and present a prototype called Countersign that can strengthen existing HITL systems with consumer mobile devices.
arXiv AI
Sep 2
Delegation Without Trust: An Empirical Gap Analysis of Identity, Authorization, and Runtime Governance in Multi-Agent LLM Systems
The paper examines the security challenges of delegating authority to autonomous LLM agents that act on users’ behalf. It introduces a threat model with four adversaries and eight security requirements, demonstrates that current frameworks (LangGraph, CrewAI, AutoGen, MCP) fail to meet these standards, and presents an authorization broker that blocks all identified threats with minimal overhead. The broker is shown to resist numerous attacks and limits compromised sub‑agents to their delegated tasks, and its principles are implemented in VotalAI’s LLM Shield.
arXiv AI
Oct 21
Local Allow, Global Deny: Why Per-Action Authorization Does Not Compose in LLM Agents
arXiv:2610.27620v1 Announce Type: cross Abstract: Autonomous LLM agents act through tools under per-action authorization: a local guard screens each tool call in isolation. We show that local authori...
arXiv Machine Learning
Jun 25
The Unfireable Safety Kernel: Execution-Time AI Alignment for AI Agents and Other Escapable AI Systems
arXiv:2606. 26057v1 Announce Type: cross Abstract: AI agents are granted access to tools, APIs, and other infrastructure, making them active principals in those systems.
arXiv AI
Jul 13
The LLMbda Calculus: AI Agents, Conversations, and Information Flow
arXiv:2602. 20064v2 Announce Type: replace-cross Abstract: Large language models are increasingly deployed as agents: they plan, call tools, read untrusted data, and act on the results.
arXiv AI
Oct 23
PerMIT: Securing Persistent Memory for LLM Agents via Machine-Checked Integrity Theorems and Scalable Community Trust
PerMIT is a memory architecture that provides provable integrity guarantees for persistent memory used by large language model agents. It limits trust to user‑typed inputs and values confirmed at a gate, mediating untrusted environment‑derived values through a finite interface while preserving multi‑turn planning and tool choice. Two machine‑checked integrity theorems in Lean 4 ensure that only gate‑confirmed data can influence sensitive actions, and community endorsements can accumulate to reduce verification effort as the user base grows.