# Please Don't Kill My Vibe: Empowering Agents with Data Flow Control

arXiv: https://arxiv.org/abs/2512.05374 · HTML: https://mlsanity.com/paper/2512.05374/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 5 Dec 2025.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 5 of 5)

- [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://mlsanity.com/paper/2408.03314/) (2024)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Design Patterns for Securing LLM Agents against Prompt Injections](https://mlsanity.com/paper/2506.08837/) (2025)
- [Systematically Analyzing Prompt Injection Vulnerabilities in Diverse LLM Architectures](https://mlsanity.com/paper/2410.23308/) (2024)
- [The Free Termination Property of Queries Over Time](https://mlsanity.com/paper/2502.00222/) (2025)

## Built on by (most cited first, 4 of 4)

- [Designing for Doubt: The Case for Informed Abstention in Autonomous Agents](https://mlsanity.com/paper/2606.02965/) (2026)
- [PrismaDV: Automated Task-Aware Data Unit Test Generation](https://mlsanity.com/paper/2604.21765/) (2026)
- [Agentic Data Environments](https://mlsanity.com/paper/2607.07397/) (2026)
- [Environment Steering: Using Data Flow Control to Improve Agent Utility and Safety](https://mlsanity.com/paper/2609.35807/) (2026)

## Abstract (arXiv)

The promise of Large Language Model (LLM) agents is to perform complex, stateful tasks. This promise is stunted by significant risks - policy violations, process corruption, and security flaws - that stem from the lack of visibility and mechanisms to manage undesirable data flows produced by agent actions. Today, agent workflows are responsible for enforcing these policies in ad hoc ways. Just as data validation and access controls shifted from the application to the DBMS, freeing application developers from these concerns, we argue that systems should support Data Flow Controls (DFCs) and enforce DFC policies natively. This paper describes early work developing a portable instance of DFC for DBMSes and outlines a broader research agenda toward DFC for agent ecosystems.