This paper analyzes the performance of agentic LLM systems in complex binary reverse engineering, identifying key limitations such as handling obfuscation and token constraints, and proposing future d…

This paper systematizes two decades of hardware reverse engineering research by analyzing 187 publications, identifying key technical methods and recommending improvements for reproducibility, standar…

This paper explores the use of large language models for reverse engineering tasks and presents a targeted training approach to improve their performance.

The paper introduces LLM4CodeRE, a domain-adaptive LLM framework that significantly improves bidirectional code reverse engineering by unifying assembly-to-source and source-to-assembly translation.

The paper proposes and evaluates a novel embedding model for bidirectional function association between source code and decompiled/stripped code, significantly outperforming existing models.

The paper introduces REBench, a comprehensive, standardized benchmark dataset designed to enable fair and rigorous evaluation of Large Language Models (LLMs) on complex binary reverse engineering task…

This paper investigates prompt injection attacks targeting software reverse engineering AI agents, demonstrating detection and defense strategies against both direct and obfuscated attacks.

This paper investigates prompt injection attacks targeting software reverse engineering AI agents, demonstrating detection and defense strategies against both direct and obfuscated attacks.

The paper introduces CREBench, a comprehensive benchmark for evaluating Large Language Models (LLMs) on cryptographic binary reverse engineering, finding that while LLMs show promise, human experts st…

This paper details the design and evolution of a Hardware Reverse Engineering (HRE) course, providing key lessons for educators teaching rapidly changing technical domains.

This paper systematically investigates the effectiveness of LLM-as-a-Judge paradigm for evaluating Human-Oriented Binary Reverse Engineering (HOBRE) and introduces BinJudge, an adaptive evaluation sch…

The paper introduces Decaf, a system that uses automatic feedback and search to significantly improve the semantic correctness and accuracy of neural decompilers, boosting the decompilation rate from…

This paper analyzes various source-to-bytecode obfuscation techniques for Erlang, demonstrating that effective protection relies on exploiting the representational gaps between high-level semantics an…

The paper introduces an adversarial technique using genetic algorithms to deceive LLM-powered software reverse engineering agents, demonstrating that attackers can corrupt the analytical output of the…

The paper introduces an adversarial technique using genetic algorithms to deceive LLM-powered software reverse engineering agents, demonstrating that attackers can corrupt the analytical output of aut…

The paper develops a quantitative scoring system, CRESS, to consistently and comparably rate the severity of novel hardware reverse engineering attack scenarios, proving it is more expressive than ind…

The paper proposes CoDe-R, a two-stage framework that significantly improves the accuracy and re-executability of decompiled code generated by LLMs, achieving a new SOTA in the lightweight regime.

This paper proposes a novel decompiler-based approach called VMSpec to automate the construction of VM processor specifications, enabling existing decompilers to analyze VM-obfuscated code.

SCRIBE is a novel framework that enables reliable source-level patching of binaries by performing 'binary-aware' recompilation, successfully resolving syntactic and semantic inaccuracies inherent in d…