AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。
[Submitted on 19 Sep 2026] Title:Environment Steering: Using Data Flow Control to Improve Agent Utility and Safety View a PDF of the paper titled Environment Steering: Using Data Flow Control to Improve Agent Utility and Safety, by Charlie Summers and 6 other authors View PDF HTML (experimental) Abstract:LLM agents can make unsafe tool calls even when instructed to behave safely. Existing defenses constrain agents before execution, modify tool inputs/outputs, or rely on LLM judges; these approaches may depend on model behavior or block unsafe actions without helping the agent recover. We argue that the execution environment should instead enforce safety as the agent runs and steer it toward safe alternatives when violations occur---we call this Environment Steering. We implement this by modeling the agent and harness execution state as database tables, track the record-level data flows, and check these data flows against declarative policies during runtime. When violations are detected, policy- and context-specific feedback steers the agent toward safe trajectories. On AgentDyn, this enables the agent to improve task success rate over no-defense while achieving 0% attack success rate. Comments: 9 pages, 11 figures, REALM Workshop, EMNLP 2026 Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Databases (cs.DB) Cite as: arXiv:2609.35807 [cs.CL] (or arXiv:2609.35807v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2609.35807 arXiv-issued DOI via DataCite (pending registration) Submission history From: Charlie Summers [view email] [v1] Sat, 19 Sep 2026 20:13:58 UTC (2,212 KB) Full-text links: Access Paper: View a PDF of the paper titled Environment Steering: Using Data Flow Control to Improve Agent Utility and Safety, by Charlie Summers and 6 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL new | recent | 2026-09 Change to browse by: cs cs.AI cs.DB References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)