Adversarial robustness of LLM-based blockchain intrusion detection

Evaluate the robustness of large-language-model-based blockchain intrusion-detection systems against adversarially crafted inputs, including prompt-injection-style manipulation intended to induce misclassification.

Background

The paper observes that blockchain can preserve the integrity of a recorded detection decision but cannot establish whether the underlying decision is correct. This limitation is especially significant for large-language-model-based detectors, whose failure modes may include prompt injection, hallucination, and adversarial manipulation of their reasoning process.

The surveyed LLM-blockchain intrusion-detection work, including LQB-IDS, does not evaluate attacks specifically targeting the LLM component. Such evaluation is important because manipulated detection outputs could be immutably recorded or used to trigger automated defensive actions.

References

None of the LLM-blockchain-IDS work surveyed in Section~\ref{sec:ai-engine}, including LQB-IDS, evaluates robustness against adversarially crafted inputs designed to exploit the LLM component specifically, leaving this as a concrete, currently open gap at the intersection of LLM safety research and blockchain-IDS design.

— From Network Intrusion Detection to Blockchain-Backed Endpoint Detection and Response: Mapping the Landscape of Decentralized Detection-and-Response Architectures  (2610.01872 - Shahsavari et al., 1 Oct 2026) in Section 6.4, “Adversarial Robustness of the AI Detection Engine”