The paper introduces a novel multi-LLM orchestration system combined with symbolic execution to successfully detect memory vulnerabilities in uncompilable, incomplete Rust CVE code snippets, achieving…

Assertain is an automated framework that uses large language models and design analysis to generate high-quality, executable security assertions for hardware designs, significantly outperforming state…

SafeTune is a framework that enhances the robustness of LLMs fine-tuned for RTL code generation by detecting and mitigating data poisoning attacks, particularly those aiming to insert hardware Trojans…

The paper empirically evaluates the security quality of LLM-generated code across various prompting methods, finding that while prompting alters the structure of weaknesses, it is insufficient to reli…

This study formally verified 3,500 AI-generated code artifacts and found that a majority (55.8%) contain exploitable security vulnerabilities, regardless of the LLM used.

The paper introduces LogicEval, a systematic framework and dataset (LogicDS) to evaluate automated repair techniques for logical software vulnerabilities, finding that prompt sensitivity and context l…

Veritas is a semantically grounded framework that detects memory corruption vulnerabilities in stripped binaries by combining static analysis, LLM-based reasoning, and runtime validation, achieving hi…

FT-Pilot is a novel GNN-guided LLM framework that automatically rewrites RTL code to harden digital circuits against soft errors, providing an efficient, automated path for reliability optimization.

The paper addresses the gap in understanding real-world LLM-in-the-loop vulnerabilities by creating the LLMCVE dataset and demonstrating that these vulnerabilities are significantly harder to repair t…

The paper analyzes LLM vulnerability detection using mechanistic interpretability, finding that models primarily rely on safety detectors rather than direct vulnerability signature recognition.

AutoSOUP is a system that automates component-level memory-safety verification by generating Safety-Oriented Unit Proofs, leveraging a hybrid LLM-based architecture to overcome manual workflow limitat…

The paper introduces codebadger, a Model Context Protocol (MCP) server that integrates Joern's Code Property Graph (CPG) with LLMs, enabling large language models to perform large-scale, semantic prog…

This review analyzes the dual impact of integrating Large Language Models (LLMs) into hardware design, detailing both their transformative potential in EDA and the critical security vulnerabilities th…

The paper introduces VULNSCOUT-C, a compact, specialized transformer model that achieves state-of-the-art performance in C code vulnerability detection while maintaining low inference cost, making it…

This systematic mapping survey reviews label-efficient approaches for code vulnerability detection, synthesizing five paradigm families and providing a decision guide to navigate trade-offs.

The paper introduces HarmChip, a novel benchmark to evaluate LLM vulnerability to domain-specific hardware security threats, revealing that current safety guardrails fail against semantically disguise…

The paper introduces an automated framework demonstrating that LLM system instructions are vulnerable to encoding attacks, where structured output requests can bypass safety refusals and leak sensitiv…

This paper systematically studies how soft errors propagate during Large Language Model (LLM) inference using a novel fault-injection framework, providing critical insights and mitigation strategies f…

The paper introduces ProofLoop, a novel ReAct agent that uses a solver-in-the-loop approach to automatically generate and formally verify SystemVerilog Assertions (SVA) from natural language specifica…

The paper introduces an execution-grounded, cross-language framework that significantly improves the reliability of LLM-driven code vulnerability analysis by ensuring that all proposed fixes are confi…