arXiv (All)
open access
Environment Steering: Using Data Flow Control to Improve Agent Utility and Safety
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
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Safety in Self-Evolving LLM Agent Systems: Threats, Amplification, and Case Studies#299821
- Environment Steering: Using Data Flow Control to Improve Agent Utility and Safety#1122390
- ContainmentBench: Trace-Based Evaluation of Post-Injection Containment in Tool-Using LLM Agents#405550
- BraveGuard: From Open-World Threats to Safer Computer-Use Agents#246449
- Toward Safe LLM Agents: A Survey of Specification, Verification, and Enforcement#610092
Record · ID 1130649
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive