RadvanSec
1.04K subscribers
189 photos
27 videos
144 files
605 links
"Security is Just an Illusion"
" امنیت فقط یک توهم است "

RadvanSec.com

Youtube , Instagram : @RadvanSec
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#WebApp_Security
1. Exploiting an ORM Injection to Steal Cryptocurrency from an Online Shooter
https://blog.p1.gs/writeup/2025/07/06/Hacking-a-crypto-game
2. Delivering PHP RCE to the Local Network Servers
https://github.com/ZeroMemoryEx/PHP-CGI-INTERNAL-RCE
3. XSS in Google IDX Workstation
https://sudistark.github.io/2025/07/02/idx.html

♦️@ZeroSec_team
2
DOMino.pdf
9.6 MB
#tools
#WebApp_Security
#Offensive_security
DEF CON 33:
"The DOMino Effect:
Automated Detection and Exploitation of DOM Clobbering Vulnerability at Scale
".

]-> dynamic analysis tool to detect/exploit DOMC vulns
]-> Dataset: DOMC Gadgets Collection
]-> Research (.pdf)

// .. first dynamic analysis framework to automatically detect and exploit DOM Clobbering gadgets. Key insight is to model attacker-controlled HTML markups as Symbolic DOM - a formalized representation to define and solve DOM-related constraints with in the gadgets - so that it can be used to generate exploit HTML markups

⭐️ @Zerosec_team
👍6
openapi_sec.pdf
5.6 MB
#WebApp_Security
"A Deep Dive Into OpenAPI Security", 2024.

⭐️ @ZeroSec_team
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NodeJS_Sec_for_WebApp.pdf
2.4 MB
#Tech_book
#WebApp_Security
"Essential Node.js Security for Express Web Applications", 2023.

// This book aims to equip existing Node.js developers, both beginners and experienced, with expertise and skills in security best practices. The book takes a practical hands-on approach to the Node.js ecosystem by using a good deal of source code examples, as well as leveraging and reviewing well tested and commonly used libraries and industry security standards

⭐️ @Zerosec_team
3🔥1
In_Browser_LLM_Guided_Fuzzing.pdf
3.7 MB
"In-Browser LLM-Guided Fuzzing for Real-Time Prompt Injection Testing in Agentic AI Browsers", 2025.
]-> Complete fuzzing platform

// LLM based agents integrated into web browsers offer powerful automation of web tasks. However, they are vulnerable to indirect prompt injection attacks. We present a novel fuzzing framework that runs entirely in the browser and is guided by an LLM to automatically discover such prompt injection vulnerabilities in real time. We demonstrate that our in-browser LLM-guided fuzzer can effectively uncover prompt injection weaknesses in autonomous browsing agents while maintaining zero false positives in detection
#AIOps
#Fuzzing
#WebApp_Security

⭐️ @Zerosec_team
2👍2