# Agent Security Meets Regulatory Reality -- A Practitioner Systematization of Autonomous-Agent Threats and Controls in Regulated Financial Systems

arXiv: https://arxiv.org/abs/2606.29142 · HTML: https://mlsanity.com/paper/2606.29142/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 28 Jun 2026.

## Placed in

- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 12 of 12)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection](https://mlsanity.com/paper/2302.12173/) (2023)
- [Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents](https://mlsanity.com/paper/2505.02077/) (2025)
- [Securing Agentic AI: A Comprehensive Threat Model and Mitigation Framework for Generative AI Agents](https://mlsanity.com/paper/2504.19956/) (2025)
- [The Landscape of Prompt Injection Threats in LLM Agents: From Taxonomy to Analysis](https://mlsanity.com/paper/2602.10453/) (2026)
- [From Storage to Steering: Memory Control Flow Attacks on LLM Agents](https://mlsanity.com/paper/2603.15125/) (2026)
- [Systematization of Knowledge: Security and Safety in the Model Context Protocol Ecosystem](https://mlsanity.com/paper/2512.08290/) (2025)
- [Identity Management for Agentic AI: The new frontier of authorization, authentication, and security for an AI agent world](https://mlsanity.com/paper/2510.25819/) (2025)
- [A Systematic Survey of Security Threats and Defenses in LLM-Based AI Agents: A Layered Attack Surface Framework](https://mlsanity.com/paper/2604.23338/) (2026)
- [FinVault: Benchmarking Financial Agent Safety in Execution-Grounded Environments](https://mlsanity.com/paper/2601.07853/) (2026)
- [Caging the Agents: A Zero Trust Security Architecture for Autonomous AI in Healthcare](https://mlsanity.com/paper/2603.17419/) (2026)
- [Type-Checked Compliance: Deterministic Guardrails for Agentic Financial Systems Using Lean 4 Theorem Proving](https://mlsanity.com/paper/2604.01483/) (2026)

## Abstract (arXiv)

Large language model agents are entering regulated financial systems, yet the security literature characterizing their attack surface is almost entirely laboratory-based, and the practitioner guidance on regulated deployment is neither peer-reviewed nor connected to a formal threat model. We bridge the two from production experience. We map six established agentic threat categories namely prompt injection, identity and authorization, action auditability, tool abuse, data residency, and boundary policy enforcement onto the specific control obligations imposed by the US and the EU financial regulation (ECOA and Regulation B, the EU AI Act, GDPR Article 22, and FINRA's 2026 agent guidance), showing how legal accountability amplifies each threat relative to an unregulated deployment. We then document four architectural patterns from a production Know Your Customer deployment for a consumer credit product (A2A compliance choreography, grounded-RAG-for-audit, case-ID propagation, and an inference-boundary redaction proxy) that moved a multi-day manual process to same-day automated resolution for roughly four in five cases. Finally, we report three negative results, including two control failures surfaced only by internal audit and a population of legitimate applicants the automated pipeline cannot serve. Securing agents under regulation, we conclude, is less about novel attack classes than about making auditability, least-privilege authorization, and boundary policy enforcement real at production scale -- requirements current agent frameworks leave to the deploying engineer.