Lazy Hacker

  An adaptive LLM security assessment framework for authorised red teams. Burp-Suite-style intruder for Large Language Model applications — with adaptive intelligence, 633+ curated payloads, session replay, and evidence-grade reporting. Download:   https://github.com/crazywifi/llm-Intruder What is LLM-Intruder? LLM-Intruder is an open-source framework for systematically assessing the security of Large Language Model (LLM) applications — chatbots, copilots, RAG systems, AI agents, MCP tool servers, and any application that exposes an LLM to users. It combines the breadth of a curated attack library (49 catalogues, 633+ payloads, 22 mutation strategies, 20 encoding techniques) with the depth of an adaptive hunting loop that learns from each response. You point it at a target — a web chat UI, an OpenAI-compatible API, a Burp Suite request — and it probes, mutates, and reports. Purpose Find bypass conditions in LLM applications before attackers do: Prompt injection and jailbreak...

URL:   https://crazywifi.github.io/Redteam_LLM_Injection_payloads/ Project Overview The LLM Red Team Payload Vault is a comprehensive, production-ready library of adversarial prompt injection vectors designed for security researchers, pentest teams, and AI developers. This repository consolidates 700+ unique attack payloads merged from industry-standard red teaming tools (such as Promptfoo, Garak, and PyRIT) and exhaustive academic research compendiums. The dataset is organized into 15 specialized categories, providing a structured approach to testing the robustness of Large Language Models (LLMs) and autonomous agents. Key Features Massive Dataset: Includes 700+ deduplicated payloads ranging from simple overrides to complex cryptographic and multi-turn attacks. Structured Taxonomy: Payloads are categorized based on the OWASP Top 10 for LLM Applications, including Direct Injection (LLM01), Sensitive Info Disclosure (LLM02), and System Prompt Leakage (LLM07). Complex Attack Chains: ...