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# 🧠🔥 AI Algorithmic Red Teaming
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A framework and methodology for proactively testing, validating, and hardening AI systems against adversarial threats, systemic risks, and unintended behaviors.
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## 🚩 What is Algorithmic Red Teaming?
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AI Algorithmic Red Teaming is a structured, adversarial testing process that simulates real-world attacks and misuse scenarios against AI models, systems, and infrastructure. It mirrors traditional cybersecurity red teaming — but focuses on probing the **behavior, bias, robustness, and resilience** of machine learning (ML) and large language model (LLM) systems.
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---
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## 🎯 Objectives
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- **Expose vulnerabilities** in AI systems through adversarial testing
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- **Evaluate robustness** to adversarial inputs, data poisoning, and model extraction
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- **Test system alignment** with security, privacy, and ethical policies
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- **Validate controls** against overreliance, excessive agency, prompt injection, and insecure plugin design
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- **Contribute to AI safety and governance** efforts by documenting and mitigating critical risks
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---
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## 🧩 Key Components
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### 1. Attack Categories
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- **Prompt Injection & Jailbreaking**
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- **Model Evasion (Adversarial Examples)**
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- **Data Poisoning & Backdoor Attacks**
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- **Model Extraction (Stealing)**
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- **Inference Manipulation & Overreliance**
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- **Sensitive Information Disclosure**
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- **Insecure Plugin / Tool Use**
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- **RAG-Specific Attacks (Embedding Manipulation, Vector Leakage)**
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### 2. Evaluation Metrics
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- Attack success rate
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- Confidence degradation
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- Output alignment drift
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- Hallucination frequency
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- Guardrail bypass percentage
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- Latency and inference impact
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### 3. Test Surfaces
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- LLM APIs (OpenAI, Claude, Gemini, open-source)
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- Embedding models and vector databases
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- Retrieval-Augmented Generation (RAG) systems
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- Plugin-based LLM architectures
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- Agentic AI frameworks (e.g., AutoGPT, LangGraph)
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- Proprietary models in deployment environments
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---
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## 🛠️ Tools & Frameworks
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Look under the [AI Security Tools section](https://github.com/The-Art-of-Hacking/h4cker/blob/master/ai_research/ai_security_tools.md).
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