mlsanityData to 14 Oct 2026

A Contemporary Survey of Large Language Model Assisted Program Analysis

arXiv 2502.18474 ↗First posted 5 Feb 2025cs.SE cs.AIcited by 11

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Abstract (arXiv)

The increasing complexity of software systems has driven significant advancements in program analysis, as traditional methods unable to meet the demands of modern software development. To address these limitations, deep learning techniques, particularly Large Language Models (LLMs), have gained attention due to their context-aware capabilities in code comprehension. Recognizing the potential of LLMs, researchers have extensively explored their application in program analysis since their introduction. Despite existing surveys on LLM applications in cybersecurity, comprehensive reviews specifically addressing their role in program analysis remain scarce. In this survey, we systematically review the application of LLMs in program analysis, categorizing the existing work into static analysis, dynamic analysis, and hybrid approaches. Moreover, by examining and synthesizing recent studies, we identify future directions and challenges in the field. This survey aims to demonstrate the potential of LLMs in advancing program analysis practices and offer actionable insights for security researchers seeking to enhance detection frameworks or develop domain-specific models.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 87 of its 177 references that are in the map. First posted on arXiv 5 Feb 2025.

Builds on

87 of 177 references.

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38 more
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50 most cited of 87 All 87 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 6; 2026: 5.

11 papers.

  1. 2025State-Aware Perturbation Optimization for Robust Deep Reinforcement Learningcited by 8
  2. 2025Robust Deep Reinforcement Learning in Robotics via Adaptive Gradient-Masked Adversarial Attackscited by 7
  3. 2026Learning When to Remember: Risk-Sensitive Contextual Bandits for Abstention-Aware Memory Retrieval in LLM-Based Coding Agentscited by 3
  4. 2025SFL-LEO: Asynchronous Split-Federated Learning Design for LEO Satellite-Ground Network Frameworkcited by 2
  5. 2025Empirical Evaluation of Concept Drift in ML-Based Android Malware Detectioncited by 2
  6. 2026A Decompilation-Driven Framework for Malware Detection with Large Language Modelscited by 2
  7. 2026Satisfiability Solving with LLMs: A Matched-Pair Evaluation of Reasoning Capabilitycited by 2
  8. 2025Automated detection of atomicity violations in large-scale systemscited by 1
  9. 2025UIXPOSE: Mobile Malware Detection via Intention-Behaviour Discrepancy Analysiscited by 0
  10. 2026Detecting Vulnerability-Inducing Commits via Multi-Stage Reasoning with LLM-Based Agentscited by 0
  11. 2026Hiding in Plain Sight: An Effective Physical Adversarial Patch Attack against Visual-Infrared Fused Face Detectioncited by 0