arXiv:2610.28212v1 Announce Type: cross Abstract: ExeBench is one of the most widely used benchmarks for evaluating LLM-based decompilers. We analyze ExeBench and identify nine issues that affect how...
The paper examines how large‑language‑model (LLM) decompilers, which produce clean, idiomatic C code, are currently evaluated mainly on recompilability and passing shipped tests. It shows that these metrics can mask significant behavioral differences: a decompiled function may recompile and pass all tests yet diverge on other inputs or lose disclosed vulnerabilities. To address this, the authors propose Decompile‑Diverge, a behavioral oracle that synthesizes drivers, fuzzes inputs, and compares the decompiled code’s behavior to the original, revealing divergences in up to 13% of cases and exposing gaps in current evaluation suites.
arXiv:2606. 26490v1 Announce Type: cross Abstract: Static verification tools can assure industrial scale software, but require significant human labor to write specifications.
The paper investigates whether the hidden activations of large language models (LLMs) contain signals about the vulnerability of C/C++ code when the code is provided as context. By extracting prefill token activations from four LLMs and training small MLP probes, the authors achieve an average F1 score of 41.7% across four benchmarks, with the best probe matching state‑of‑the‑art fine‑tuned classifiers on the Devign dataset. The results suggest that a coding LLM’s internal representation can inform vulnerability detection, opening the door to lightweight, model‑native screening methods.
arXiv:2609.10397v1 Announce Type: cross Abstract: Exception Related Code (ERC), which includes throw statements, conditions (if statements) that guard those throw statements, and try/catch blocks, is...
arXiv:2610. 23044v1 Announce Type: new Abstract: Rust is increasingly adopted as a memory-safe successor to C, and transpilers such as \crust{} can migrate entire C projects while preserving their behavior.
arXiv:2607. 07738v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly applied to reverse-engineering tasks, and recent threat-intelligence reporting shows them operating inside live offensive-security workflows.
The paper introduces ReviveBench, a benchmark designed to evaluate coding agents’ ability to revive non‑running software and reconstruct industrial engines from open specifications. It comprises two families of tasks—revival (ten tasks addressing dependency issues, missing modules, legacy builds, and GPU models) and reconstruction (thirteen tasks covering numerical, geometric, hardware, and transactional systems). The benchmark uses hidden verifiers calibrated against native environments, engineering tools, or reference implementations, and the authors report that the strongest evaluated model passes all revival tasks and most reconstruction tasks, while also uncovering verifier defects that highlight measurement error in executable verification.
arXiv:2607. 06125v1 Announce Type: cross Abstract: Neural decompilation is increasingly studied as a code-generation problem, yet its evaluation methodology remains underdeveloped for modern languages.
arXiv:2608. 02639v1 Announce Type: cross Abstract: Production prompts rarely carry a single instruction.
arXiv:2606. 20128v1 Announce Type: cross Abstract: Benchmarks for LLM-generated GPU kernels (KernelBench, TritonBench, GEAK) score correctness through fixed-shape, small-sample allclose-style checks.
The paper discusses a workflow for repairing decompiled C code that goes beyond mere recompilation. It uses compiler and linker diagnostics to guide initial repair, then applies smoke checks and official tests to ensure the recompiled binary behaves as expected. In a study of 104 Coreutils binaries, 87.5% successfully recompiled and passed the test gate, while a small portion failed to recompile or still failed tests.