# Authenticated Workflows: A Systems Approach to Protecting Agentic AI

arXiv: https://arxiv.org/abs/2602.10465 · HTML: https://mlsanity.com/paper/2602.10465/

## 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 14 of its 39 references that are in the map.
- First posted on arXiv 11 Feb 2026.

## Placed in

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

## Builds on (most cited first, 14 of 14)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://mlsanity.com/paper/2307.15043/) (2023)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [Jailbroken: How Does LLM Safety Training Fail?](https://mlsanity.com/paper/2307.02483/) (2023)
- [Llama Guard: LLM-based Input-Output Safeguard for Human-AI Conversations](https://mlsanity.com/paper/2312.06674/) (2023)
- [Ignore Previous Prompt: Attack Techniques For Language Models](https://mlsanity.com/paper/2211.09527/) (2022)
- [Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned](https://mlsanity.com/paper/2209.07858/) (2022)
- [Baseline Defenses for Adversarial Attacks Against Aligned Language Models](https://mlsanity.com/paper/2309.00614/) (2023)
- [Prompt Injection attack against LLM-integrated Applications](https://mlsanity.com/paper/2306.05499/) (2023)
- [Identifying the Risks of LM Agents with an LM-Emulated Sandbox](https://mlsanity.com/paper/2309.15817/) (2023)
- [Formalizing and Benchmarking Prompt Injection Attacks and Defenses](https://mlsanity.com/paper/2310.12815/) (2023)
- [AI Agents Under Threat: A Survey of Key Security Challenges and Future Pathways](https://mlsanity.com/paper/2406.02630/) (2024)
- [From Prompt Injections to Protocol Exploits: Threats in LLM-Powered AI Agents Workflows](https://mlsanity.com/paper/2506.23260/) (2025)
- [A Survey on Agentic Security: Applications, Threats and Defenses](https://mlsanity.com/paper/2510.06445/) (2025)

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

- [Toward Secure LLM Agents: Threat Surfaces, Attacks, Defenses, and Evaluation](https://mlsanity.com/paper/2606.10749/) (2026)
- [Bounded Agents: Delegation Security for Multi-Agent AI Systems](https://mlsanity.com/paper/2608.15888/) (2026)
- [AI Identity: Standards, Gaps, and Research Directions for AI Agents](https://mlsanity.com/paper/2604.23280/) (2026)
- [The Importance of Out-of-Band Metadata for Safe Autonomous Agents: The Redpanda Agentic Data Plane](https://mlsanity.com/paper/2605.29082/) (2026)

## Abstract (arXiv)

Agentic AI systems automate enterprise workflows but existing defenses--guardrails, semantic filters--are probabilistic and routinely bypassed. We introduce authenticated workflows, the first complete trust layer for enterprise agentic AI. Security reduces to protecting four fundamental boundaries: prompts, tools, data, and context. We enforce intent (operations satisfy organizational policies) and integrity (operations are cryptographically authentic) at every boundary crossing, combining cryptographic elimination of attack classes with runtime policy enforcement. This delivers deterministic security--operations either carry valid cryptographic proof or are rejected. We introduce MAPL, an AI-native policy language that expresses agentic constraints dynamically as agents evolve and invocation context changes, scaling as O(log M + N) policies versus O(M x N) rules through hierarchical composition with cryptographic attestations for workflow dependencies. We prove practicality through a universal security runtime integrating nine leading frameworks (MCP, A2A, OpenAI, Claude, LangChain, CrewAI, AutoGen, LlamaIndex, Haystack) through thin adapters requiring zero protocol modifications. Formal proofs establish completeness and soundness. Empirical validation shows 100% recall with zero false positives across 174 test cases, protection against 9 of 10 OWASP Top 10 risks, and complete mitigation of two high impact production CVEs.