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 Kops, an extension interface for eBPF that allows userspace compilers and kernel modules to introduce new operations without modifying the kernel core, improving performance and r…

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…

The paper introduces Heimdall, an automated pipeline that uses LLMs and formal verification to safely and automatically migrate legacy, potentially buggy eBPF programs written in C to memory-safe Rust…

The paper proposes a new binary format that embeds compiler-generated metadata into executables, making the binary structure more transparent and enabling reliable analysis, instrumentation, and recom…

The paper introduces Spaghetti Architect, a tool that generates controlled code datasets for machine learning models by deliberately adding redundancy, messiness, and difficulty.

The paper proposes TRAVEL, a framework for automated C-to-Rust translation using Monte Carlo Tree Search and reinforcement learning, improving accuracy and success rate.

This paper evaluates the capability of large language models to produce machine-checked proofs of refinement between executable code and its high-level specification in the context of Ethereum Virtual…

SAILOR automates the construction of symbolic execution harnesses by combining static analysis and LLM-based synthesis, significantly improving the scalability and effectiveness of vulnerability disco…

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 proposes a portable inlining-prediction framework for lightweight systems, using production compiler diagnostics, an extractor, and a trained predictor.

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…

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…

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.

KVerus is a retrieval-augmented system that significantly improves the scalability and resilience of formal verification for Rust code by managing complex cross-module dependencies and adapting to cod…

This paper proposes an automated static verification method for a subset of WebAssembly using a CHCs satisfiability solver, addressing challenges of handling indirect function calls and analyzing larg…

The paper proposes a general, compiler-integrated framework for secure content composition that minimizes the syntactic difference between secure and insecure coding practices.