Weekly Report 2026-04-19
工作任务
-
调研所有的内核漏洞扫描的sota工具
-
对其中的某些sota工具进行实验
工作进展
调研内核扫描sota工具
首先通过布尔表达式从四大顶会中搜索相关论文,找到了130篇论文,然后将这130篇论文进行筛选,最终筛选出50篇,并分类,在分类的过程中也了解到了整个漏洞扫描工具的整体研究方向,以下是分类
一、静态SOTA工具
1. LLM直接分析
• LLMxCPG: Context-Aware Vulnerability Detection through Code Property Graph-Guided Large Language Models (USENIX Security 2025)
2. 符号执行
• KRover: A Symbolic Execution Engine for Dynamic Kernel Analysis (ICSE 2023)
• Symbolic Execution for Dynamic Kernel Analysis (ICSE 2024)
3. 静态Checker
通用:
• KNighter: Transforming Static Analysis with LLM-Synthesized Checkers (arXiv 2025)
• Type-Based Call Graph: Unleashing the Power of Type-Based Call Graph Construction by Using Regional Pointer Information (USENIX Security 2024)
特定:
• APP-Miner: Detecting API Misuses via Automatically Mining API Path Patterns (ICSE 2024)
• LR-Miner: Static Race Detection in OS Kernels by Mining Locking Rules (USENIX Security 2024)
• Interrupt Deadlock: When Threads Meet Interrupts: Effective Static Detection of Interrupt-Based Deadlocks in Linux (USENIX Security 2024)
• Inconsistent MM: Detecting Kernel Memory Bugs through Inconsistent Memory Management Intention Inferences (USENIX Security 2024)
• FIRE: Combining Multi-Stage Filtering with Taint Analysis for Scalable Recurring Vulnerability Detection (USENIX Security 2024)
• Error Spec Inference: Inference of Error Specifications and Bug Detection Using Structural Similarities (USENIX Security 2024)
• API Post-Handling: Detecting API Post-Handling Bugs Using Code and Description in Patches (USENIX Security 2023)
• Sailor: Save What Must Be Saved: Secure Context Switching with Sailor (USENIX Security 2025)
• ISA Analysis: Automatic ISA Analysis for Secure Context Switching (arXiv 2025)
• KallGraph: Redefining Indirect Call Analysis with KallGraph (ICSE 2025)
二、动态SOTA工具
1. SoK综述
• SoK: Prudent Evaluation Practices for Fuzzing (IEEE S&P 2024)
• SoK: Where to Fuzz? Assessing Target Selection Methods in Directed Fuzzing (ISSTA 2024)
• A Qualitative Analysis of Fuzzer Usability and Challenges (ICSE 2025)
2. Fuzz Driver生成
• Utopia: Automatic Generation of Fuzz Driver Using Unit Tests (ICSE 2023)
• Prompt Fuzzing: Prompt Fuzzing for Fuzz Driver Generation (FSE 2024)
3. Syzkaller类工具
(a) Syzlang依赖文件生成
• SyzSpec (ICSE 2025)
• SyzForge (arXiv 2025)
• SyzGen++ (IEEE S&P 2024)
• SyzDescribe (IEEE S&P 2023)
(b) Fuzz目标选择
• SyzParam (ICSE 2025)
• ProphetFuzz (FSE 2024)
• KLAUS (USENIX Security 2023)
(c) 定向Fuzz
• SyzDirect (ICSE 2023)
• SelectFuzz (IEEE S&P 2023)
• DAFL (USENIX Security 2023)
• Titan (IEEE S&P 2024)
• Critical Code Guided Directed Greybox Fuzzing for Commits (USENIX Security 2024)
• IDFuzz (USENIX Security 2025)
(d) 特定Fuzzer
• SegFuzz (IEEE S&P 2023)
• CountDown (FSE 2024)
• DDRace (USENIX Security 2023)
• Fuzzing API Error Handling Behaviors using Coverage Guided Fault Injection (ISSTA 2024)
(e) 种子/输入调度
• SyzVegas (USENIX Security 2021)
• ACTOR (USENIX Security 2023)
• FOX (FSE 2024)
• T-Scheduler (ISSTA 2024)
(f) 覆盖率
• StateFuzz (USENIX Security 2022)
• Arvin (ISSTA 2023)
• Binary Coverage for Kernel Fuzzing (FSE 2024)
• Multi-target Coverage-based Greybox Fuzzing (arXiv 2026)
• SyzRisk (ISSTA 2024)
(g) 内存检测
• Evaluating the Effectiveness of Memory Safety Sanitizers (ICSE 2025)
• CMASan: Custom Memory Allocator-Aware Address Sanitizer (ICSE 2025)
• RangeSanitizer (USENIX Security 2025)
4. 污点分析
• AirTaint: Making Dynamic Taint Analysis Faster and Easier (ICSE 2024)
5. 集成Fuzzer
• autofz: Automated Fuzzer Composition at Runtime (USENIX Security 2023)
对其中的某些sota工具进行实验
由于上述实现有50篇,人工一篇篇复现不现实,于是求助于ai工具,通过现有工具和一些改进,实现整个论文复现+实验的自动化workflow:
首先分为复现和实验,复现是对论文的直接复现,而实验是针对复现成功的实验,进行“传统工具不能检测出riscv漏洞”的实验,按照之前的实验设计,分为3个实验,exp123
使用claude-code的无头版本(即可以一直运行而不会进行用户询问)aris(auto research in sleep),编写repro和exp两份skills,让aris抓取最近一篇未复现的论文进行复现或者实验论文进行实验,并且通过worktree,让这个行为并行
(按照ai声称)最终我整理结果如下:
对于reproduce的论文:
成功复现的有18篇
Partial复现或者复现结果不符合预期的有12篇
复现失败(比如找不到论文的代码仓库)的有19篇
还未开始复现的有4篇
对于进行experiments的实验:
进行exp1的有5篇,4篇完成,1篇基本完成
进行exp3及其变体的有8篇,3篇完成,3篇基本完成,2篇被判定为用户态fuzzer失败
进行exp2及其变体的有1篇,失败1篇
未进行的实验总数是7篇
(exp1及是之前的smatch实验,静态分析内核并与代码量进行比例验证
exp2是之前的召回率实验,之前也是使用sota工具knight进行验证
exp3是之前的覆盖率实验,之前使用syzkaller进行验证)
具体的数据还未进行人工整理
Comments
Loading…