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gray-swan-mitm-interaction
Guide AI assistants through Gray Swan MITM challenges using Playwright browser automation to interact with AI agent intermediaries. Use when working on Gray Swan Machine-in-the-Middle CTF challenges.
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multi-model-orchestration
Orchestrate workflows across multiple AI models (Perplexity, GPT, Grok, Claude, Gemini) for comprehensive security research, competition execution, and strategic analysis using GUI interfaces or API automation
GitHub
gray-swan-mitm-general
Complete guide for Gray Swan MITM challenges across all waves. Includes AI agent profiling, defense bypass strategies, domain-specific handling, and platform troubleshooting. Use for ANY Gray Swan MITM challeng...
GitHub
gray-swan-ipi-wave-2-executor
Execute Indirect Prompt Injection attacks for Gray Swan AI Arena Wave 2 with pre-built payloads, model profiling, and evidence collection automation
GitHub
gray-swan-wave3-mitm
Execute Gray Swan Wave 3 MITM challenges across 10 challenges and 90 AI models using Playwright automation, tiered execution strategy, and compliance framing. Use for Wave 3 healthcare, critical infrastructure,...
GitHub
gray-swan-wave3-mitm-updated
Updated Wave 3 MITM execution skill incorporating Nov 18 platform stability discoveries, domain-specific AI defenses, and WordPress container isolation. Use for Wave 3 challenges with realistic success rates an...
GitHub
ai-ctf-generic
Execute AI security CTF challenges across any competition platform with adaptable workflows for indirect prompt injection, jailbreaks, agent exploitation, and evidence collection with research-grounded techniqu...
GitHub
gray-swan-competition
Execute Gray Swan AI Arena indirect prompt injection (IPI) and machine-in-the-middle (MITM) challenges with optimized payloads, reconnaissance workflows, and evidence collection for CTF competition success
GitHub
payload-generator
Generate optimized indirect prompt injection, H-CoT, and multi-layer attack payloads for AI security testing and CTF competitions with automated family selection and success rate optimization
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.