How can I defend against fairness attacks in Hyperledger Fabric?
Adversary-Augmented Simulation for Fairness Evaluation and Defense in Hyperledger Fabric
This paper explores fairness attacks on Hyperledger Fabric (HF), a blockchain system, using a multi-agent simulation with a programmable adversary. It focuses on transaction reordering attacks where an adversary manipulates the order of transactions to gain an advantage, violating fairness properties. A mitigation mechanism using peer-endorsement metadata to inform orderer transaction sequencing is proposed and evaluated, demonstrating increased robustness against these attacks.
For LLM-based multi-agent systems, the key takeaway is the vulnerability of decentralized systems to adversarial manipulation, even when individual agents operate within established rules and tolerances. The research demonstrates how an adversary can exploit timing and ordering within a multi-agent system. The proposed mitigation leveraging metadata and order-based consensus algorithms offers a practical strategy for enhancing robustness and fairness in multi-agent interactions, highlighting the importance of incorporating ordering and timing considerations into agent communication and decision-making protocols. The simulation framework used here could also be adapted to study LLM-based multi-agent interactions and the impact of malicious agents within those systems.