多Agent协同的内核漏洞自动化复现框架

## 中文描述

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### 项目名称  
**多Agent协同的内核漏洞自动化复现框架**

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### 项目描述  
#### （1）相关背景  
随着内核漏洞复杂度的提升（如CVE-2021-43267的多阶段竞争条件漏洞），传统漏洞复现方法面临三大挑战：  
1. **跨阶段漏洞建模难**：漏洞触发需精准控制内存操作、并发调度、硬件状态等多维度条件。  
2. **人工经验依赖性强**：OpenEuler安全专家需耗费数周分析漏洞模式并设计PoC，尤其针对新版本kernel上线场景。  
3. **工具链割裂**：现有工具（LLM/Syzkaller/PoC生成器）缺乏协同，导致信息流断裂。

#### （2）已有的工作  
- Syzkaller支持覆盖率引导的模糊测试，但无法关联漏洞语义。  
- GPT-4o/Deepseek-R1具备代码生成能力，但未针对内核漏洞优化。  
- 学术界提出基于符号执行的漏洞复现方法（如KLEE），但效率低下。

#### （3）存在的不足  
- **单点工具局限性**：LLM生成、模糊测试、PoC验证等环节孤立运行，缺乏反馈闭环。  
- **动态环境感知弱**：无法根据Syzkaller运行时状态（如覆盖率/崩溃点）动态调整生成策略。  
- **多模态数据处理缺失**：文本型CVE描述、代码补丁、硬件事件日志未实现联合分析。

#### （4）希望改进的点  
构建多Agent协同框架，包含以下核心Agent：  
1. **CVE Analysis Agent**：  
   - 解析CVE文本（如NVD描述），提取漏洞特征（漏洞类型/触发条件/影响组件）。  
   - 输入：CVE-2021-43267描述 → 输出："TIPC协议未校验msg_section大小导致堆溢出"。  
2. **Syscall Conversion Agent**：  
   - 构建CVE中kernel的历史漏洞报告（CVE+补丁diff）与Syzkaller语法文件与成功PoC案例的数据库
   - 利用RAG技术，支持自然语言到syscall模板的转换（如"制造堆溢出"→`copy_from_user+kmalloc`组合）。 
 
3. **Sequence Generation Agent**：  
   - 根据漏洞特征生成候选syscall序列， 用于syzkaller检测特定CVE漏洞。

4. **Fuzzing Orchestration Agent**：  
   - 动态调度Syzkaller实例，支持并行测试多组候选序列。  
   - 实时监控KASAN/KCSAN报告，捕获use-after-free、data race等异常。

5. **PoC Synthesis Agent**：  
   - 将触发崩溃的syscall序列转换为可验证的C代码PoC。

#### （5）最终项目实现的目标  
1. 实现5个功能Agent的协同工作流，复现10+历史漏洞（含CVE-2021-43267）。  
2. 对比社区解决方案，漏洞复现效率提升8倍（平均耗时从72h降至9h）。  
3. 提交多Agent框架至OpenEuler社区，支持API扩展（新增Agent接入）。

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### 项目难度  
**进阶**

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### 技术领域标签  
**漏洞挖掘 | AI安全 | 模糊测试**

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### 编程语言标签  
**Python | Go**

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### 项目产出要求  
- **核心Agent**：  
  - 5个标准化Agent模块（Docker容器化部署）。  
- **测试验证**：  
  - 包含5种漏洞类型的测试集（堆溢出/竞争条件/整数溢出等）。  
- **文档输出**：  
  - 《多Agent协同协议设计规范》。  
  - 《Agent扩展开发指南》。

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### 项目技术要求  
- 掌握多Agent系统设计模式（如swarm、langgraph）。  
- 精通Syzkaller内部机制（包括进程调度/崩溃捕获）。   
- （加分项）有RAG或Agent相关开发经验。

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### 项目成果提交仓库  
**主仓库**：https://gitee.com/openeuler/ai-fuzzing  
**子目录**：  
- `/agents`（各Agent实现代码）  
- `/orchestrator`（调度中间件）  
- `/evaluation`（测试用例与性能报告）

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### 预估工时  
**650小时**  
| 阶段                | 工时  | 关键产出                     |  
|---------------------|-------|----------------------------|  
| Agent架构设计       | 150h | Agent职责划分、任务分解       |  
| 核心Agent开发       | 300h | 5个Agent功能实现            |  
| Syzkaller协同调度优化        | 120h | 负载均衡算法、故障恢复机制   |  
| 文档与社区适配      | 80h  | 开发者手册、社区集成测试     |

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### 项目备注  
1. **基础设施需求**：  
   - 大模型API接口（实现Agent功能）。  
   - 昇腾910C（RAG以及相关功能实现）。  
2. **合规性**：    
   - 训练数据需过滤敏感信息（如未公开漏洞细节）。  
3. **社区协作**：
   - 与OpenEuler安全委员会联合设计Agent，社区代码合并。
   - 请求OpenEuler安全委员会，建立openEuler/ai-fuzzing仓库。

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### 项目导师  
**SeanLmax | sean.lixiang@aliyun.com**  
通过多Agent协同框架，可实现漏洞复现流程的完全自动化与智能化，为OpenEuler构建下一代AI驱动的安全研究基础设施。

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## English Description
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### Project Name  
**Multi-Agent Collaborative Framework for Automated Kernel Vulnerability Reproduction**

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### Project Description  
#### (1) Context  
With the increasing complexity of kernel vulnerabilities (e.g., multi-stage race condition vulnerabilities like CVE-2021-43267), traditional vulnerability reproduction methods face three major challenges:  
1. **Difficulty in Cross-Phase Vulnerability Modeling**: Precise control over multi-dimensional conditions (memory operations, concurrency scheduling, hardware states) is required to trigger vulnerabilities.  
2. **Over-Reliance on Manual Expertise**: OpenEuler security experts spend weeks analyzing vulnerability patterns and designing PoCs, especially for newly released kernel versions.  
3. **Toolchain Fragmentation**: Existing tools (LLM/Syzkaller/PoC generators) lack collaboration, leading to fragmented information flow.

#### (2) Existing Work  
- Syzkaller supports coverage-guided fuzzing but cannot correlate vulnerability semantics.  
- GPT-4o/Deepseek-R1 have code generation capabilities but are not optimized for kernel vulnerabilities.  
- Academic methods like symbolic execution (e.g., KLEE) are proposed for vulnerability reproduction but suffer from inefficiency.

#### (3) Limitations  
- **Isolated Tool Limitations**: LLM generation, fuzzing, and PoC validation operate independently without feedback loops.  
- **Weak Dynamic Environment Awareness**: Inability to dynamically adjust generation strategies based on Syzkaller runtime states (e.g., coverage/crash points).  
- **Missing Multimodal Data Processing**: No joint analysis of textual CVE descriptions, code patches, and hardware event logs.

#### (4) Proposed Improvements  
Build a multi-agent collaborative framework with the following core agents:  
1. **CVE Analysis Agent**:  
   - Parses CVE texts (e.g., NVD descriptions) to extract vulnerability features (type/trigger conditions/impacted components).  
   - Input: CVE-2021-43267 description → Output: "Heap overflow in TIPC protocol due to unvalidated msg_section size."  
2. **Syscall Conversion Agent**:  
   - Constructs a database of historical kernel vulnerability reports (CVE + patch diffs), Syzkaller syntax files, and successful PoC cases.  
   - Uses RAG to convert natural language to syscall templates (e.g., "create heap overflow" → `copy_from_user+kmalloc` combinations).

3. **Sequence Generation Agent**:  
   - Generates candidate syscall sequences for Syzkaller to detect specific CVE vulnerabilities.

4. **Fuzzing Orchestration Agent**:  
   - Dynamically schedules Syzkaller instances to test multiple candidate sequences in parallel.  
   - Monitors KASAN/KCSAN reports in real-time to capture use-after-free, data races, etc.

5. **PoC Synthesis Agent**:  
   - Converts crash-triggering syscall sequences into verifiable C code PoCs.

#### (5) Project Goals  
1. Implement a collaborative workflow of 5 functional agents to reproduce 10+ historical vulnerabilities (including CVE-2021-43267).  
2. Achieve 8x efficiency improvement compared to community solutions (average time reduced from 72h to 9h).  
3. Submit the multi-agent framework to the OpenEuler community with API extensibility (supporting new agent integration).

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### Project Difficulty  
**Advanced**

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### Technical Domains  
**Vulnerability Discovery | AI Security | Fuzz Testing**

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### Programming Languages  
**Python | Go**

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### Deliverables  
- **Core Agents**:  
  - 5 standardized agent modules (Docker containerized).  
- **Test Validation**:  
  - Test sets covering 5 vulnerability types (heap overflow/race condition/integer overflow, etc.).  
- **Documentation**:  
  - *Multi-Agent Collaboration Protocol Design Specifications*.  
  - *Agent Extension Development Guide*.

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### Technical Requirements  
- Proficiency in multi-agent system design patterns (e.g., swarm, langgraph).  
- Deep understanding of Syzkaller internals (process scheduling/crash capture).  
- (Bonus) Experience with RAG or agent development.

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### Repository  
**Main Repo**: https://gitee.com/openeuler/ai-fuzzing  
**Subdirectories**:  
- `/agents` (agent implementations)  
- `/orchestrator` (scheduling middleware)  
- `/evaluation` (test cases & performance reports)

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### Estimated Effort  
**650 hours**  
| Phase                  | Hours | Key Deliverables              |  
|------------------------|-------|-------------------------------|  
| Agent Architecture Design | 150h | Agent role definitions, task decomposition |  
| Core Agent Development | 300h  | Implementation of 5 agents    |  
| Syzkaller Coordination Optimization | 120h | Load balancing algorithms, fault recovery |  
| Documentation & Community Integration | 80h | Developer manuals, community tests |

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### Notes  
1. **Infrastructure Requirements**:  
   - LLM API access (for agent functionalities).  
   - Ascend 910C (for RAG implementation).  
2. **Compliance**:  
   - Training data must filter sensitive information (e.g., unpublished vulnerability details).  
3. **Community Collaboration**:  
   - Joint design with OpenEuler Security Committee.  
   - Request to establish the `openEuler/ai-fuzzing` repository.

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### Mentor  
**SeanLmax | sean.lixiang@aliyun.com**  
This multi-agent collaborative framework enables fully automated and intelligent vulnerability reproduction, establishing next-generation AI-driven security research infrastructure for OpenEuler.

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