Source Code Foundation Models are Transferable Binary Analysis Knowledge Bases

• URL: http://arxiv.org/abs/2405.19581v2
• Date: Wed, 30 Oct 2024 16:12:36 GMT
• Title: Source Code Foundation Models are Transferable Binary Analysis Knowledge Bases
• Authors: Zian Su, Xiangzhe Xu, Ziyang Huang, Kaiyuan Zhang, Xiangyu Zhang,
• Abstract summary: Human-Oriented Binary Reverse Engineering aims to lift binary code to human-readable content relevant to source code. We introduce a novel probe-and-recover framework that incorporates a binary-source encoder-decoder model and black-box LLMs for binary analysis.
• Score: 9.422025563792818
• License:
• Abstract: Human-Oriented Binary Reverse Engineering (HOBRE) lies at the intersection of binary and source code, aiming to lift binary code to human-readable content relevant to source code, thereby bridging the binary-source semantic gap. Recent advancements in uni-modal code model pre-training, particularly in generative Source Code Foundation Models (SCFMs) and binary understanding models, have laid the groundwork for transfer learning applicable to HOBRE. However, existing approaches for HOBRE rely heavily on uni-modal models like SCFMs for supervised fine-tuning or general LLMs for prompting, resulting in sub-optimal performance. Inspired by recent progress in large multi-modal models, we propose that it is possible to harness the strengths of uni-modal code models from both sides to bridge the semantic gap effectively. In this paper, we introduce a novel probe-and-recover framework that incorporates a binary-source encoder-decoder model and black-box LLMs for binary analysis. Our approach leverages the pre-trained knowledge within SCFMs to synthesize relevant, symbol-rich code fragments as context. This additional context enables black-box LLMs to enhance recovery accuracy. We demonstrate significant improvements in zero-shot binary summarization and binary function name recovery, with a 10.3% relative gain in CHRF and a 16.7% relative gain in a GPT4-based metric for summarization, as well as a 6.7% and 7.4% absolute increase in token-level precision and recall for name recovery, respectively. These results highlight the effectiveness of our approach in automating and improving binary code analysis.

Related papers

• MM-RLHF: The Next Step Forward in Multimodal LLM Alignment [59.536850459059856]
  We introduce MM-RLHF, a dataset containing $mathbf120k$ fine-grained, human-annotated preference comparison pairs. We propose several key innovations to improve the quality of reward models and the efficiency of alignment algorithms. Our approach is rigorously evaluated across $mathbf10$ distinct dimensions and $mathbf27$ benchmarks.
  arXiv  Detail & Related papers  (2025-02-14T18:59:51Z)
• Correctness Assessment of Code Generated by Large Language Models Using Internal Representations [4.32362000083889]
  We introduce OPENIA, a novel framework to assess the correctness of code generated by Large Language Models (LLMs) Our empirical analysis reveals that these internal representations encode latent information, which strongly correlates with the correctness of the generated code. OPENIA consistently outperforms baseline models, achieving higher accuracy, precision, recall, and F1-Scores with up to a 2X improvement in standalone code generation.
  arXiv  Detail & Related papers  (2025-01-22T15:04:13Z)
• A Progressive Transformer for Unifying Binary Code Embedding and Knowledge Transfer [15.689556592544667]
  We introduce ProTST, a novel transformer-based methodology for binary code embedding. ProTST employs a hierarchical training process based on a unique tree-like structure. Results show that ProTST yields an average validation score (F1, MRR, and Recall@1) improvement of 14.8% compared to traditional two-stage training.
  arXiv  Detail & Related papers  (2024-12-15T13:04:29Z)
• OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models [70.72097493954067]
  Large language models (LLMs) for code have become indispensable in various domains, including code generation, reasoning tasks and agent systems. While open-access code LLMs are increasingly approaching the performance levels of proprietary models, high-quality code LLMs remain limited. We introduce OpenCoder, a top-tier code LLM that not only achieves performance comparable to leading models but also serves as an "open cookbook" for the research community.
  arXiv  Detail & Related papers  (2024-11-07T17:47:25Z)
• AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data [64.69872638349922]
  We present AlchemistCoder, a series of Code LLMs with enhanced code generation and generalization capabilities fine-tuned on multi-source data. We propose incorporating the data construction process into the fine-tuning data as code comprehension tasks, including instruction evolution, data filtering, and code review.
  arXiv  Detail & Related papers  (2024-05-29T16:57:33Z)
• How Far Have We Gone in Binary Code Understanding Using Large Language Models [51.527805834378974]
  We propose a benchmark to evaluate the effectiveness of Large Language Models (LLMs) in binary code understanding. Our evaluations reveal that existing LLMs can understand binary code to a certain extent, thereby improving the efficiency of binary code analysis.
  arXiv  Detail & Related papers  (2024-04-15T14:44:08Z)
• BinaryAI: Binary Software Composition Analysis via Intelligent Binary Source Code Matching [8.655595404611821]
  We introduce BinaryAI, a novel binary-to-source SCA technique with two-phase binary source code matching to capture both syntactic and semantic code features. Our experimental results demonstrate the superior performance of BinaryAI in terms of binary source code matching and the downstream SCA task.
  arXiv  Detail & Related papers  (2024-01-20T07:57:57Z)
• CodeGen2: Lessons for Training LLMs on Programming and Natural Languages [116.74407069443895]
  We unify encoder and decoder-based models into a single prefix-LM. For learning methods, we explore the claim of a "free lunch" hypothesis. For data distributions, the effect of a mixture distribution and multi-epoch training of programming and natural languages on model performance is explored.
  arXiv  Detail & Related papers  (2023-05-03T17:55:25Z)
• UniASM: Binary Code Similarity Detection without Fine-tuning [2.2329530239800035]
  We propose a novel rich-semantic function representation technique to ensure the model captures the intricate nuances of binary code. We introduce the first UniLM-based binary code embedding model, named UniASM, which includes two newly designed training tasks. The experimental results show that UniASM outperforms the state-of-the-art (SOTA) approaches on the evaluation datasets.
  arXiv  Detail & Related papers  (2022-10-28T14:04:57Z)
• Towards Accurate Binary Neural Networks via Modeling Contextual Dependencies [52.691032025163175]
  Existing Binary Neural Networks (BNNs) operate mainly on local convolutions with binarization function. We present new designs of binary neural modules, which enables leading binary neural modules by a large margin.
  arXiv  Detail & Related papers  (2022-09-03T11:51:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.