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

奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
人人都是产品经理
人人都是产品经理
爱范儿
爱范儿
aimingoo的专栏
aimingoo的专栏
博客园 - 叶小钗
H
Help Net Security
Microsoft Security Blog
Microsoft Security Blog
The Cloudflare Blog
S
SegmentFault 最新的问题
小众软件
小众软件
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
博客园 - 司徒正美
The GitHub Blog
The GitHub Blog
量子位
H
Hackread – Cybersecurity News, Data Breaches, AI and More
V
V2EX
Martin Fowler
Martin Fowler
博客园 - 【当耐特】
J
Java Code Geeks
D
DataBreaches.Net
云风的 BLOG
云风的 BLOG
F
Fortinet All Blogs
Blog — PlanetScale
Blog — PlanetScale
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
Artemis: Toward Accurate Detection of Server-Side Request...
Yuchen Ji, Ting Dai, Zhichao Zhou, Yutian Tang, Jingzhu He · 2025-02-28 · via cs.SE updates on arXiv.org

Server-side request forgery (SSRF) vulnerabilities are inevitable in PHP web applications. Existing static tools in detecting vulnerabilities in PHP web applications neither contain SSRF-related features to enhance detection accuracy nor consider PHP's dynamic type features. In this paper, we present Artemis, a static taint analysis tool for detecting SSRF vulnerabilities in PHP web applications. First, Artemis extracts both PHP built-in and third-party functions as candidate source and sink functions. Second, Artemis constructs both explicit and implicit call graphs to infer functions' relationships. Third, Artemis performs taint analysis based on a set of rules that prevent over-tainting and pauses when SSRF exploitation is impossible. Fourth, Artemis analyzes the compatibility of path conditions to prune false positives. We have implemented a prototype of Artemis and evaluated it on 250 PHP web applications. Artemis reports 207 true vulnerable paths (106 true SSRFs) with 15 false positives. Of the 106 detected SSRFs, 35 are newly found and reported to developers, with 24 confirmed and assigned CVE IDs.