惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

美团技术团队
T
The Blog of Author Tim Ferriss
月光博客
月光博客
阮一峰的网络日志
阮一峰的网络日志
Engineering at Meta
Engineering at Meta
量子位
I
InfoQ
Jina AI
Jina AI
Microsoft Security Blog
Microsoft Security Blog
H
Help Net Security
H
Hackread – Cybersecurity News, Data Breaches, AI and More
G
Google Developers Blog
J
Java Code Geeks
Recent Announcements
Recent Announcements
aimingoo的专栏
aimingoo的专栏
小众软件
小众软件
V
V2EX
腾讯CDC
P
Proofpoint News Feed
A
About on SuperTechFans
爱范儿
爱范儿
U
Unit 42
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
Last Week in AI
Last Week in AI

cs.SE updates on arXiv.org

VLA Foundry: A Unified Framework for Training Vision-Language-Action Models Evaluating LLM-Generated Obfuscated XSS Payloads for Machine Learning-Based Detection Do Agents Dream of Root Shells? Partial-Credit Evaluation of LLM Agents in Capture the Flag Challenges Refute-or-Promote: An Adversarial Stage-Gated Multi-Agent Review Methodology for High-Precision LLM-Assisted Defect Discovery From Particles to Perils: SVGD-Based Hazardous Scenario Generation for Autonomous Driving Systems Testing Choose Your Own Adventure: Non-Linear AI-Assisted Programming with EvoGraph Human-Machine Co-Boosted Bug Report Identification with Mutualistic Neural Active Learning LLMSniffer: Detecting LLM-Generated Code via GraphCodeBERT and Supervised Contrastive Learning Neurosymbolic Repo-level Code Localization CodeMMR: Bridging Natural Language, Code, and Image for Unified Retrieval Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Verification Modulo Tested Library Contracts The Semi-Executable Stack: Agentic Software Engineering and the Expanding Scope of SE Scaling Test-Time Compute for Agentic Coding AI-Assisted Requirements Engineering: An Empirical Evaluation Relative to Expert Judgment From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Evolution Atropos: Improving Cost-Benefit Trade-off of LLM-based Agents under Self-Consistency with Early Termination and Model Hotswap Vibe-Coding: Feedback-Based Automated Verification with no Human Code Inspection, a Feasibility Study Benchmarks for Trajectory Safety Evaluation and Diagnosis in OpenClaw and Codex: ATBench-Claw and ATBench-Codex Bounded Autonomy for Enterprise AI: Typed Action Contracts and Consumer-Side Execution AIPC: Agent-Based Automation for AI Model Deployment with Qualcomm AI Runtime Analyzing Chain of Thought (CoT) Approaches in Control Flow Code Deobfuscation Tasks Asking What Matters: Reward-Driven Clarification for Software Engineering Tasks Prompt-Driven Code Summarization: A Systematic Literature Review LinuxArena: A Control Setting for AI Agents in Live Production Software Environments LLMs taking shortcuts in test generation: A study with SAP HANA and LevelDB Large Language Models to Enhance Business Process Modeling: Past, Present, and Future Trends CollabCoder: Plan-Code Co-Evolution via Collaborative Decision-Making for Efficient Code Generation Sentiment analysis for software engineering: How far can zero-shot learning (ZSL) go? Learning from Change: Predictive Models for Incident Prevention in a Regulated IT Environment
Static Exploration of Taint-Style Vulnerabilities Found b...
Bhargava Shastry, Federico Maggi, Fabian Yamaguchi, Konrad Rieck · 2017-06-01 · via cs.SE updates on arXiv.org

Taint-style vulnerabilities comprise a majority of fuzzer discovered program faults. These vulnerabilities usually manifest as memory access violations caused by tainted program input. Although fuzzers have helped uncover a majority of taint-style vulnerabilities in software to date, they are limited by (i) extent of test coverage; and (ii) the availability of fuzzable test cases. Therefore, fuzzing alone cannot provide a high assurance that all taint-style vulnerabilities have been uncovered. In this paper, we use static template matching to find recurrences of fuzzer-discovered vulnerabilities. To compensate for the inherent incompleteness of template matching, we implement a simple yet effective match-ranking algorithm that uses test coverage data to focus attention on those matches that comprise untested code. We prototype our approach using the Clang/LLVM compiler toolchain and use it in conjunction with afl-fuzz, a modern coverage-guided fuzzer. Using a case study carried out on the Open vSwitch codebase, we show that our prototype uncovers corner cases in modules that lack a fuzzable test harness. Our work demonstrates that static analysis can effectively complement fuzz testing, and is a useful addition to the security assessment tool-set. Furthermore, our techniques hold promise for increasing the effectiveness of program analysis and testing, and serve as a building block for a hybrid vulnerability discovery framework.