SAFEFLOW: A Principled Protocol for Trustworthy and Transactional
  Autonomous Agent Systems
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.
6847957e3ec10bdd8ab4de5c
Peiran Li
6847957e3ec10bdd8ab4de5d
Xinkai Zou
claimed_verified
2025-06-12T12:43:03.715Z
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XINKAI ZOU
user
jayzou3773
6847957e3ec10bdd8ab4de5e
Zhuohang Wu
6847957e3ec10bdd8ab4de5f
Ruifeng Li
6847957e3ec10bdd8ab4de60
Shuo Xing
claimed_verified
2025-06-10T08:43:55.652Z
62df9ee3c7e6f74fc92a1351
/avatars/2a4f60ecc2fc4be2a8ac21c3f2ceb3cc.svg
Shuo Xing
user
shuoxing
6847957e3ec10bdd8ab4de61
Hanwen Zheng
6847957e3ec10bdd8ab4de62
Zhikai Hu
6847957e3ec10bdd8ab4de63
Yuping Wang
6847957e3ec10bdd8ab4de64
Haoxi Li
6847957e3ec10bdd8ab4de65
Qin Yuan
6847957e3ec10bdd8ab4de66
Yingmo Zhang
6847957e3ec10bdd8ab4de67
Zhengzhong Tu
claimed_verified
2025-07-10T09:12:57.908Z
62548d5fef3debb2ddf91217
/avatars/14975b45568f9c399c92c3986b6ce83e.svg
Zhengzhong Tu
user
vztu
SAFEFLOW: A Principled Protocol for Trustworthy and Transactional
  Autonomous Agent Systems
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.
SAFEFLOW secures autonomous agents by enforcing fine-grained information flow control and ensuring reliability in multi-agent, adversarial environments through transactional execution and secure scheduling.
large language models vision-language models autonomous agents secure information flow reliability multi-agent coordination fine-grained information flow control provenance integrity confidentiality transactional execution conflict resolution secure scheduling shared state write-ahead logging rollback secure caches benchmark suite adversarial operational conditions