SAFEFLOW: A Principled Protocol for Trustworthy and Transactional Autonomous Agent Systems
Journal:
arXiv
Published Date:
Jun 9, 2025
Abstract
Recent advances in large language models (LLMs) and vision-language models
(VLMs) have enabled powerful autonomous agents capable of complex reasoning and
multi-modal tool use. Despite their growing capabilities, today's agent
frameworks remain fragile, lacking principled mechanisms for secure information
flow, reliability, and multi-agent coordination. In this work, we introduce
SAFEFLOW, a new protocol-level framework for building trustworthy LLM/VLM-based
agents. SAFEFLOW enforces fine-grained information flow control (IFC),
precisely tracking provenance, integrity, and confidentiality of all the data
exchanged between agents, tools, users, and environments. By constraining LLM
reasoning to respect these security labels, SAFEFLOW prevents untrusted or
adversarial inputs from contaminating high-integrity decisions. To ensure
robustness in concurrent multi-agent settings, SAFEFLOW introduces
transactional execution, conflict resolution, and secure scheduling over shared
state, preserving global consistency across agents. We further introduce
mechanisms, including write-ahead logging, rollback, and secure caches, that
further enhance resilience against runtime errors and policy violations. To
validate the performances, we built SAFEFLOWBENCH, a comprehensive benchmark
suite designed to evaluate agent reliability under adversarial, noisy, and
concurrent operational conditions. Extensive experiments demonstrate that
agents built with SAFEFLOW maintain impressive task performance and security
guarantees even in hostile environments, substantially outperforming
state-of-the-art. Together, SAFEFLOW and SAFEFLOWBENCH lay the groundwork for
principled, robust, and secure agent ecosystems, advancing the frontier of
reliable autonomy.
