Unrolled cryptographic hardware architectures are increasingly deployed to maximize throughput, inherently intro- ducing massive algorithmic noise floors that frequently thwart traditional temporal Side-Channel Analysis (SCA). However, the reliance on structural combinational noise as a standalone coun- termeasure remains underexplored against adaptive, AI-driven profiling. This work presents a novel autonomous framework uti- lizing a Large Language Model (LLM) agent to orchestrate...

The increasing deployment of large language models (LLMs) in high-stakes domains demands infrastructure to ensure trust in artificial intelligence (AI)-generated outputs and actions. Users often struggle to validate results from LLMs because their reasoning is opaque and possibly beyond human comprehension. This paper introduces proof-carrying output (PCO), a framework in which an AI system returns an answer accompanied by a machine-checkable proof. We define φ-compliance formally (see the...

The rapid advancement of Large Language Models has given rise to autonomous LLM-based agents capable of complex reasoning and execution. As these agents transition from isolated operation to collaborative ecosystems, we witness the emergence of the Agent-to-Agent (A2A) network, a paradigm where heterogeneous agents autonomously coordinate to solve multi-step tasks. While these networks may offer better task performance compared to simply using one agent to complete the entire task, they...

LLM-based agents, which interleave large language model inference with external tool calls, are increasingly deployed in high-stakes settings. In real-world deployments, each model inference and provider-hosted tool execute behind the provider's API. Even when the agent loop runs on the user's device, these provider-executed steps still remain opaque to the user. This opacity creates an end-to-end integrity gap: a malicious provider may substitute the advertised model or fabricate tool...

Non-invasive security constitutes an essential component of hardware security, primarily involving side-channel analysis (SCA), with various international standards explicitly mandating rigorous testing. However, current SCA assessments rely on manual expert procedures, causing critical issues: inconsistent results due to expert variability, error-prone multi-step testing, and high costs with IP leakage risks for manufacturers lacking in-house expertise. Automated SCA tools that...

Ensuring the security of complex system-on-chips (SoCs) designs is a critical imperative, yet traditional verification techniques struggle to keep pace due to significant challenges in automation, scalability, comprehensiveness, and adaptability. The advent of large language models (LLMs), with their remarkable capabilities in natural language understanding, code generation, and advanced reasoning, presents a new paradigm for tackling these issues. Moving beyond monolithic models, an agentic...

Current hardware security verification processes predominantly rely on manual threat modeling and test plan generation, which are labor-intensive, error-prone, and struggle to scale with increasing design complexity and evolving attack methodologies. To address these challenges, we propose ThreatLens, an LLM-driven multi-agent framework that automates security threat modeling and test plan generation for hardware security verification. ThreatLens integrates retrieval-augmented generation...