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Autonomous Agents on Blockchains: Standards, Execution Models, and Trust Boundaries

Authors: Saad Alqithami

Published: 2026-01-08

arXiv ID: 2601.04583v1

Added to Library: 2026-01-09 03:02 UTC

📄 Abstract

Advances in large language models have enabled agentic AI systems that can reason, plan, and interact with external tools to execute multi-step workflows, while public blockchains have evolved into a programmable substrate for value transfer, access control, and verifiable state transitions. Their convergence introduces a high-stakes systems challenge: designing standard, interoperable, and secure interfaces that allow agents to observe on-chain state, formulate transaction intents, and authorize execution without exposing users, protocols, or organizations to unacceptable security, governance, or economic risks. This survey systematizes the emerging landscape of agent-blockchain interoperability through a systematic literature review, identifying 317 relevant works from an initial pool of over 3000 records. We contribute a five-part taxonomy of integration patterns spanning read-only analytics, simulation and intent generation, delegated execution, autonomous signing, and multi-agent workflows; a threat model tailored to agent-driven transaction pipelines that captures risks ranging from prompt injection and policy misuse to key compromise, adversarial execution dynamics, and multi-agent collusion; and a comparative capability matrix analyzing more than 20 representative systems across 13 dimensions, including custody models, permissioning, policy enforcement, observability, and recovery. Building on the gaps revealed by this analysis, we outline a research roadmap centered on two interface abstractions: a Transaction Intent Schema for portable and unambiguous goal specification, and a Policy Decision Record for auditable, verifiable policy enforcement across execution environments. We conclude by proposing a reproducible evaluation suite and benchmarks for assessing the safety, reliability, and economic robustness of agent-mediated on-chain execution.

🔍 Key Points

  • Introduction of enhanced Constitutional Classifiers (CCs) that provide robustness against jailbreak attempts with significant computational cost reduction.
  • Development of exchange classifiers that analyze model outputs in context to mitigate reconstruction and obfuscation attacks.
  • Implementation of a two-stage classifier cascade optimizing computational efficiency by screening traffic with a lightweight classifier before escalating to a more complex system.
  • Utilization of efficient linear probe classifiers that reduce resource demands while maintaining detection capabilities against harmful outputs.
  • Extensive red-teaming efforts demonstrating the effectiveness of the proposed system in resisting universal jailbreaks, achieving a low refusal rate of 0.05%.

💡 Why This Paper Matters

This paper presents a significant advancement in the robustness of large language models against jailbreaking attempts, highlighting a new, efficient architecture for Constitutional Classifiers that balances robustness and production feasibility. By integrating innovative classification strategies and thorough testing, the findings provide a pathway for deploying safer AI systems in real-world applications.

🎯 Why It's Interesting for AI Security Researchers

This paper is highly relevant to AI security researchers as it addresses critical vulnerabilities in language models, offering practical defenses against malicious attempts to elicit harmful or sensitive information. The novel methods, such as exchange classifiers and efficient linear probes, not only enhance system robustness but also illustrate important considerations for future AI safety measures. The extensive empirical validation through red-teaming emphasizes the importance of ongoing security assessment efforts, making it a valuable reference in the field of AI-enabled safety mechanisms.

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