# Empirical Study on Large Language Models in Vulnerability Analysis of Automotive Binary Programs

> This paper explores how large language models (LLMs) are applied in the field of automotive software security, analyzing their capabilities, limitations, and practical application prospects in binary vulnerability detection.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-04-22T01:00:28.000Z
- 最近活动: 2026-04-22T04:07:51.917Z
- 热度: 145.9
- 关键词: 大语言模型, 汽车软件安全, 二进制漏洞分析, 嵌入式系统, 静态分析, 智能网联汽车, ECU安全
- 页面链接: https://www.zingnex.cn/en/forum/thread/llm-github-sea-pre-an-empirical-study-of-large-language-models-for-vulnerability-analysis-i
- Canonical: https://www.zingnex.cn/forum/thread/llm-github-sea-pre-an-empirical-study-of-large-language-models-for-vulnerability-analysis-i
- Markdown 来源: floors_fallback

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## [Introduction] Core Summary of the Empirical Study on Large Language Models in Vulnerability Analysis of Automotive Binary Programs

This paper conducts an empirical study on the application of large language models (LLMs) in vulnerability analysis of automotive binary programs, exploring their capabilities, limitations, and practical application prospects in the field of automotive software security. The study finds that while LLMs show potential in vulnerability detection, they have problems such as insufficient cross-architecture generalization and high false positive rates; integrating with traditional static analysis tools can improve detection coverage and accuracy, providing a new path for automotive software security testing.

## Research Background and Unique Challenges of Automotive Software Security

### Research Background and Significance
With the development of intelligent connected vehicles, the security of in-vehicle software has become a focus. Modern cars have a large number of ECUs and complex embedded software; traditional binary vulnerability analysis relies on expert experience and static tools, facing challenges such as low efficiency, high false positives, and difficulty in handling complex code. LLMs have great potential in code understanding and security detection, and their application in automotive binary vulnerability analysis is expected to break through traditional bottlenecks.

### Unique Challenges of Automotive Software Security
Automotive embedded systems have characteristics such as heterogeneous architectures (ARM, PowerPC, etc.), many closed-source binary components, special communication protocols (CAN/LIN/FlexRay), and resource constraints. General-purpose tools have limited effectiveness, putting forward special requirements for the application of LLMs.

## Research Methodology Design

This study constructs a test dataset for automotive binary programs (open-source firmware + actual vulnerability cases) and designs a multi-dimensional evaluation framework (accuracy, false positive rate, efficiency, cross-architecture generalization, etc.). The experiment selects general code models and security-fine-tuned models; the evaluation tasks cover common vulnerabilities such as buffer overflow and integer overflow, and examines the robustness of binaries with different optimization levels, obfuscation/packing.

## Key Findings and Performance Analysis

The research results show that: security-fine-tuned models perform better than general models in identifying typical memory vulnerabilities; however, their performance declines when handling highly optimized binaries, as compiler optimizations interfere with pattern recognition; cross-architecture generalization ability is limited, with insufficient analysis of rare automotive processors; understanding of automotive-specific protocols and state machine logic is weak, requiring domain knowledge verification.

## False Positive Analysis and Interpretability Issues

Main false positive scenarios: misjudging normal boundary checks as vulnerabilities, over-alerting on complex pointer operations, misunderstanding compiler protection code as attack payloads. In terms of interpretability, LLMs can generate natural language reasoning, but there is a "hallucination" phenomenon (explanations do not match code logic), so the credibility of reasoning needs to be improved.

## Comparison with Traditional Methods and Integration Strategies

Traditional static tools have clear rules and low false positives, but are difficult to handle unknown vulnerabilities and complex code. LLMs have strong generalization ability and can identify uncovered vulnerability variants, but have high false positives. The hybrid process uses LLMs as a supplementary layer: static tools initially screen for clear vulnerabilities, and LLMs focus on high-complexity suspicious areas. Experiments show that this method can maintain a low false positive rate and improve detection coverage.

## Practical Deployment Considerations and Future Research Directions

Deployment needs to address issues such as computing resource costs, model update and maintenance (to adapt to the evolution of technology stacks), data privacy and intellectual property protection. Future directions: developing lightweight dedicated models, establishing standardized evaluation benchmarks, and exploring deep integration with formal verification.