445 matches found
raptor v3.1.0
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offensive-security-labs
Offensive Security Labs A collection of hands-on offensive cy...
AI Can Find Bugs, But Human Knowledge Still Proves Them
Artificial intelligence AI is changing offensive security, but it has not changed the standard that matters most: a finding has to be proven before it becomes useful. AI-assisted tools can read code quickly, generate payloads, summarize attack surfaces, explain unfamiliar APIs, and run repetitive...
Red-Teaming the Agentic Red-Team
The use of agentic systems to perform offensive security operations has moved from a theoretical possibility to a commoditized capability. However, while the community has focused on creating more and more capable agents, less attention has been allocated to assessing the security of those system...
OSCP-PEN200
🛡️ OSCP / PEN-200 Master Pentesting Database !License: MIT...
Fulcrum-OSINT-monitor
FULCRUM — Architecture Technique v3.1 Vue d'ensemble FULC...
offensive-claude-813
Offensive Security Research Config for Claude Code !TIP...
offensive-claude-982
Offensive Security Research Config for Claude Code !TIP...
offensive-claude-490
Offensive Security Research Config for Claude Code !TIP...
arsenal-tools
Arsenal — CTF & Pentest Toolkit Collection de 198 outils of...
exploit-validator
$repo Production-grade offensive security tool for Purpose...
A New Framework for Cybersecurity Refusals in AI Agents
Agentic scaffolds have dramatically improved LLM performance on complex, long-horizon tasks, yielding both broad benefits and amplified risks in domains like cybersecurity. Existing benchmarks for AI agents in cybersecurity focus mainly on measuring proficiency--how effectively agents can complet...
offensive-claude
Offensive Security Research Config for Claude Code A comprehe...
portofolio_DWForSec
DwF — Cybersecurity Portfolio A professional cybersecurity po...
web-app-pentest-playbook
Web Application Pentest Playbook A structured methodology and...
01-Pentesting-and-Offensive-Security
No d...
Introducing Penetration Test Findings: Unified Offensive Security in Wiz
Streamline pen-testing by unifying findings from bug bounties, manual audits, and Wiz Red Agent into a single, context-rich view...
Automation-Exploit: A Multi-Agent LLM Framework for Adaptive Offensive Security with Digital Twin-Based Risk-Mitigated Exploitation
The offensive security landscape is highly fragmented: enterprise platforms avoid memory-corruption vulnerabilities due to Denial of Service DoS risks, Automatic Exploit Generation AEG systems suffer from semantic blindness, and Large Language Model LLM agents face safety alignment filters and...