SafeFlow: Semantic Information-Flow Control for Blocking Malicious Propagation in Multi-Agent Systems
Multi-agent systems improve capability through task decomposition and role specialization, but these same mechanisms introduce an important safety blind spot: a harmful objective can be fragmented into locally plausible subtasks, allowing malicious intent to evade detection by any single agent. This is a growing social-impact challenge: systems handling sensitive information or consequential tools can turn routine delegation into unauthorized disclosure or unsafe action. We argue that this failu
Record details
Published: 28 July 2026
Source: arXiv red teaming query
Category: Research
Topics: Agents & autonomy · Transparency
Retrieved: 29 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
SafeFlow: Semantic Information-Flow Control for Blocking Malicious Propagation in Multi-Agent Systems
arXiv red teaming query · 28 July 2026
Separating Capability from Permission: A Governance Framework for Agentic AI Autonomy Levels
arXiv cs.CY · 28 July 2026
Accountable yet Anonymous AI Agents - Split-Knowledge Binding in National Agent-Identity Layer in China
arXiv cs.CY · 28 July 2026
Toward an Organizational Science of Multi-Agent LLM Systems: Decoupling Who, How, and Which Algorithm
arXiv · 28 July 2026
UrbanTrace: LLM-Assisted Discovery and Semantics-Aware Integration of Spatial Data
arXiv cs.HC · 27 July 2026
From Obligation to Specification: A Survey on Validating EU AI Act Requirements in RE
arXiv cs.CY · 27 July 2026
How to cite this record
ethics.ai (28 July 2026), “SafeFlow: Semantic Information-Flow Control for Blocking Malicious Propagation in Multi-Agent Systems,” evidence record 14471, https://ethics.ai/record/14471 (originally published by arXiv red teaming query).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.