Pretraining on Call Graphs: When Binary Analysis Tasks Profit From Context

• URL: http://arxiv.org/abs/2608.02084v1
• Date: Mon, 03 Aug 2026 11:44:09 GMT
• Title: Pretraining on Call Graphs: When Binary Analysis Tasks Profit From Context
• Abstract summary: We conduct experiments using a range of graph-based models on the embeddings generated by two state-of-the-art binary function embedding models.<n>We find that optimizing for semantic similarity tasks correlates with worse performance on syntactic tasks.
• Score: 7.347989843033034
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Binary function embedding models are trained to encode the semantics of binary code in such a way that they can be generalized to a variety of reverse engineering tasks, such as binary code search, vulnerability detection, or malware classification. While many models only take the function in question as contextual input, there have been successful attempts to improve function embeddings by leveraging information from the call graph. In this study, we dissect the implications of these embedding refinements. We conduct experiments using a range of graph-based models on the embeddings generated by two state-of-the-art binary function embedding models. Integrating inter-procedural context, we show that improvements on binary code similarity detection (BCSD) will not necessarily generalize to downstream tasks, neither of semantic nor of syntactic nature. More generally, we find that optimizing for semantic similarity tasks correlates with worse performance on syntactic tasks. By conducting an explanatory analysis on the dataset, we find that the call graph-based enhancements significantly enhance the robustness of embeddings, particularly in scenarios where the initial models struggle. Furthermore, we observe that the added context is more beneficial for namespace-related functions than for those focused on individual logic, confirming that the call graph can be leveraged most effectively in context-dependent scenarios.

Related papers

• Combating Spurious Correlations in Graph Interpretability via Self-Reflection [4.81017678027464]
  Interpretable graph learning is a popular research topic in machine learning.<n>One of the most challenging is the Spurious-Motif benchmark, introduced at ICLR 2022.<n>We propose a self-reflection framework that can be integrated with existing interpretable graph learning methods.
  arXiv  Detail & Related papers  (2026-01-16T06:31:16Z)
• Cross-modal Retrieval Models for Stripped Binary Analysis [62.89251403093734]
  BinSeek is the first two-stage cross-modal retrieval framework for stripped binary code analysis.<n>It consists of two models: BinSeekEmbedding is trained on large-scale dataset to learn the semantic relevance of the binary code.<n>BinSeek-Reranker learns to carefully judge the relevance of the candidate code to the description with context augmentation.
  arXiv  Detail & Related papers  (2025-12-11T07:58:10Z)
• Learning Binarized Representations with Pseudo-positive Sample Enhancement for Efficient Graph Collaborative Filtering [35.82405808653398]
  We study the problem of graph representation binarization for efficient collaborative filtering.<n>Our findings indicate that explicitly mitigating information loss at various stages of embedding binarization has a significant positive impact on performance.<n>Compared to its predecessor BiGeaR, BiGeaR++ introduces a fine-grained inference distillation mechanism and an effective embedding sample synthesis approach.
  arXiv  Detail & Related papers  (2025-06-03T11:11:43Z)
• Binary Code Similarity Detection via Graph Contrastive Learning on Intermediate Representations [52.34030226129628]
  Binary Code Similarity Detection (BCSD) plays a crucial role in numerous fields, including vulnerability detection, malware analysis, and code reuse identification. In this paper, we propose IRBinDiff, which mitigates compilation differences by leveraging LLVM-IR with higher-level semantic abstraction. Our extensive experiments, conducted under varied compilation settings, demonstrate that IRBinDiff outperforms other leading BCSD methods in both One-to-one comparison and One-to-many search scenarios.
  arXiv  Detail & Related papers  (2024-10-24T09:09:20Z)
• Know Your Neighborhood: General and Zero-Shot Capable Binary Function Search Powered by Call Graphlets [0.7646713951724013]
  This paper proposes a novel graph neural network architecture combined with a novel graph data representation called call graphlets. A specialized graph neural network model operates on this graph representation, learning to map it to a feature vector that encodes semantic binary code similarities. Experimental results show that the combination of call graphlets and the novel graph neural network architecture achieves comparable or state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-06-02T18:26:50Z)
• FASER: Binary Code Similarity Search through the use of Intermediate Representations [0.8594140167290099]
  Cross-Architecture Binary Code Similarity Search has been explored in numerous studies. We propose Function as a String Encoded Representation (FASER) to create a model capable of cross architecture function search.
  arXiv  Detail & Related papers  (2023-10-05T15:36:35Z)
• Mutual Exclusivity Training and Primitive Augmentation to Induce Compositionality [84.94877848357896]
  Recent datasets expose the lack of the systematic generalization ability in standard sequence-to-sequence models. We analyze this behavior of seq2seq models and identify two contributing factors: a lack of mutual exclusivity bias and the tendency to memorize whole examples. We show substantial empirical improvements using standard sequence-to-sequence models on two widely-used compositionality datasets.
  arXiv  Detail & Related papers  (2022-11-28T17:36:41Z)
• Software Vulnerability Detection via Deep Learning over Disaggregated Code Graph Representation [57.92972327649165]
  This work explores a deep learning approach to automatically learn the insecure patterns from code corpora. Because code naturally admits graph structures with parsing, we develop a novel graph neural network (GNN) to exploit both the semantic context and structural regularity of a program.
  arXiv  Detail & Related papers  (2021-09-07T21:24:36Z)
• Learning What Makes a Difference from Counterfactual Examples and Gradient Supervision [57.14468881854616]
  We propose an auxiliary training objective that improves the generalization capabilities of neural networks. We use pairs of minimally-different examples with different labels, a.k.a counterfactual or contrasting examples, which provide a signal indicative of the underlying causal structure of the task. Models trained with this technique demonstrate improved performance on out-of-distribution test sets.
  arXiv  Detail & Related papers  (2020-04-20T02:47:49Z)
• Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction [80.38130122127882]
  We introduce 14 probing tasks targeting linguistic properties relevant to neural relation extraction (RE) We use them to study representations learned by more than 40 different encoder architecture and linguistic feature combinations trained on two datasets. We find that the bias induced by the architecture and the inclusion of linguistic features are clearly expressed in the probing task performance.
  arXiv  Detail & Related papers  (2020-04-17T09:17:40Z)

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.