VeriChat is presented, a domain-specialized conversational assistant designed to support, rather than replace, existing verification workflows by providing context-aware security guidance, significantly outperforming the leading proprietary models.
Abstract
Hardware security verification is a multi-stage process in which engineers must navigate complex design analyses, threat considerations, and verification strategies. They often need security-focused guidance, yet current verification environments provide little structured support for such assistance. Although conversational AI could offer such on-demand assistance, directly using general-purpose chatbots like ChatGPT or Gemini is risky due to their tendency to hallucinate and their reliance on static, outdated knowledge. We present VeriChat, a domain-specialized conversational assistant designed to support, rather than replace, existing verification workflows by providing context-aware security guidance. VeriChat employs a retrieval-augmented, multi-agent workflow in which three specialized agents collaboratively minimize hallucinations while improving the transparency and reliability of the response. Beyond question answering, VeriChat integrates open-source EDA tools, including Icarus Verilog, Yosys, and SymbiYosys, to perform syntax checking, synthesis analysis, simulation, and formal verification directly on user-provided RTL designs. Evaluated using a comprehensive methodology, VeriChat achieves a Faithfulness score of 87.73%, significantly outperforming the leading proprietary models. We demonstrate the framework through a hardware Trojan detection case study on an AES S-Box IP, where VeriChat autonomously identifies, simulates, and formally proves a covert key-leakage vulnerability through a multi-turn conversational workflow.
With the rapid development of large language models (LLMs), LLM-based agents are evolving from single-turn text generators into autonomous decision-making systems capable of task planning, tool invocation, memory retrieval, and environmental interaction. However, the openness of agent environments and the nondeterminism of model-driven decision logic introduce security and reliability risks that are difficult to capture with conventional software testing. Fuzzing, as an automated and scalable vulnerability discovery technique, provides a promising approach for assessing the robustness of LLM agent systems. This paper presents a systematic survey of fuzzing techniques for LLM agents. First, we analyze the distinctive architecture of LLM agents and summarize three representative threat categories: indirect prompt injection, planning hallucination, and tool abuse. Second, we review existing fuzzing methods from three perspectives: test-case generation and mutation, environment simulation and interaction monitoring, and test-oracle construction. Particular attention is paid to LLM-enhanced fuzzers that exploit the semantic understanding and generation capabilities of LLMs to improve mutation quality and attack effectiveness. Finally, we connect the main technical challenges with an actionable research roadmap, including semantic coverage, cost-aware execution, reproducible oracles, multi-agent fuzzing, gray-box guidance, and automated repair.
Yang Cao· 2026 IEEE 9th International...· 0 citations
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