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

P
Proofpoint News Feed
V
V2EX
WordPress大学
WordPress大学
Google DeepMind News
Google DeepMind News
Martin Fowler
Martin Fowler
小众软件
小众软件
Blog — PlanetScale
Blog — PlanetScale
月光博客
月光博客
The Cloudflare Blog
T
Tailwind CSS Blog
H
Help Net Security
腾讯CDC
爱范儿
爱范儿
人人都是产品经理
人人都是产品经理
H
Hackread – Cybersecurity News, Data Breaches, AI and More
The GitHub Blog
The GitHub Blog
Microsoft Security Blog
Microsoft Security Blog
Stack Overflow Blog
Stack Overflow Blog
D
DataBreaches.Net
C
Check Point Blog
量子位
酷 壳 – CoolShell
酷 壳 – CoolShell
美团技术团队
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com

cs.CR updates on arXiv.org

On the Security of Research Artifacts SafeHarbor: Hierarchical Memory-Augmented Guardrail for LLM Agent Safety Agentic Vulnerability Reasoning on Windows COM Binaries From Beats to Breaches:How Offensive AI Infers Sensitive User Information from Playlists Undetectable Backdoors in Model Parameters: Hiding Sparse Secrets in High Dimensions When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems Token-Efficient Change Detection in LLM APIs Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification Trident: Improving Malware Detection with LLMs and Behavioral Features When Alignment Isn't Enough: Response-Path Attacks on LLM Agents RefusalGuard: Geometry-Preserving Fine-Tuning for Safety in LLMs Checkerboard: A Simple, Effective, Efficient and Learning-free Clean Label Backdoor Attack with Low Poisoning Budget Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors Enhancing Linux Privilege Escalation Attack Capabilities of Local LLM Agents Defusing the Trigger: Plug-and-Play Defense for Backdoored LLMs via Tail-Risk Intrinsic Geometric Smoothing Evaluating Jailbreaking Vulnerabilities in LLMs Deployed as Assistants for Smart Grid Operations: A Benchmark Against NERC Standards Behavioral Canaries: Auditing Private Retrieved Context Usage in RL Fine-Tuning FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models Text Steganography with Dynamic Codebook and Multimodal Large Language Model TwoHamsters: Benchmarking Multi-Concept Compositional Unsafety in Text-to-Image Models Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Hardening x402: PII-Safe Agentic Payments via Pre-Execution Metadata Filtering QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits Hijacking Text Heritage: Hiding the Human Signature through Homoglyphic Substitution Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation One Word at a Time: Incremental Completion Decomposition Breaks LLM Safety
Security Testing Framework for Web Applications: Benchmar...
[Submitted on 10 Jan 2025 (v1), last revised 1 Sep 2026 (this ve · 2025-01-10 · via cs.CR updates on arXiv.org

View PDF

Abstract:The Huge growth in the usage of web applications has raised concerns regarding their security vulnerabilities, which in turn pushes toward robust security testing tools. This study compares OWASP ZAP, the leading open-source web application vulnerability scanner, across its two most recent iterations. While comparing their performance to the OWASP Benchmark, the study evaluates their efficiency in spotting vulnerabilities in the purposefully vulnerable application, OWASP Benchmark project. The research methodology involves conducting systematic scans of OWASP Benchmark using both v2.12.0 and v2.13.0 of OWASP ZAP. The OWASP Benchmark provides a standardized framework to evaluate the scanner's abilities in identifying security flaws, Insecure Cookies, Path traversal, SQL injection, and more. Results obtained from this benchmark comparison offer valuable insights into the strengths and weaknesses of each version of the tool. This study aids in web application security testing by shedding light on how well-known scanners work at spotting vulnerabilities. The knowledge gained from this study can assist security professionals and developers in making informed decisions to support their web application security status. In conclusion, this study comprehensively analyzes ZAP's capabilities in detecting security flaws using OWASP Benchmark v1.2. The findings add to the continuing debates about online application security tools and establish the framework for future studies and developments in the research field of web application security testing.

Submission history

From: Hong-Sheng Huang [view email]
[v1] Fri, 10 Jan 2025 12:07:18 UTC (1,524 KB)
[v2] Tue, 1 Sep 2026 07:00:01 UTC (1,004 KB)