arXiv AI
Safe to Resume? Breaking Execution Continuity of Agent Execution via Rollback
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arXiv AI
Sep 15
Recoverability as a System Primitive for Long-Horizon AI Agents
arXiv:2609.13672v1 Announce Type: new Abstract: AI agents can be interrupted while editing files, calling tools, or carrying out multi-step tasks. Restarting repeats completed work, but continuing fr...
arXiv AI
Sep 7
CONTINUITY: Security-Context Contracts for Composable LLM Agent Controls
The paper introduces CONTINUITY, a framework that ensures secure composition of large language model (LLM) agent controls by using assume‑guarantee contracts and authenticated security contexts. It models each component with signed root grants, provenance commitments, and other mechanisms to carry security context across transitions, formalizing end‑to‑end consequence integrity. A reference verifier and fault‑injection suite demonstrate that the full configuration prevents harmful effects while completing all benign tasks and correctly escalating ambiguous cases.
arXiv AI
4d ago
Lost in Recovery: Demystifying Recovery Behavior of Computer-Use Agents via Goal-Oriented Action Replay
arXiv:2610.27954v1 Announce Type: new Abstract: Recent advances in large (vision) language models have enabled computer-use agents that automate digital workflows via fine-grained keyboard and mouse...
arXiv AI
Jul 31
MemSecBench: Tracking Agent Memory Poisoning from Persistence to Consequence and Repair
arXiv:2607. 27080v1 Announce Type: cross Abstract: Memory systems allow agents to retain and reuse information from past interactions, but they can also let malicious content persist.
arXiv AI
Oct 15
TRAP: Tool-result Replay Attack for Probing Misbinding in LLM Agents
arXiv:2610.16333v1 Announce Type: new Abstract: As AI agents increasingly act through external tools, tool results are no longer simple outputs but execution artifacts that must remain correctly asso...
arXiv AI
Sep 2
SoK: When Safe Agents Fail Together: The Security of Multi Agent LLM Systems
arXiv:2609.00595v1 Announce Type: cross Abstract: Safe agents can fail together. Multi-agent LLM systems (MAS) move information, state, decisions, and authority across principal boundaries, creating...