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| 1 | +# 🧠🔥 AI Algorithmic Red Teaming |
| 2 | + |
| 3 | +A framework and methodology for proactively testing, validating, and hardening AI systems against adversarial threats, systemic risks, and unintended behaviors. |
| 4 | + |
| 5 | +## 🚩 What is Algorithmic Red Teaming? |
| 6 | + |
| 7 | +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. |
| 8 | + |
| 9 | +--- |
| 10 | + |
| 11 | +## 🎯 Objectives |
| 12 | + |
| 13 | +- **Expose vulnerabilities** in AI systems through adversarial testing |
| 14 | +- **Evaluate robustness** to adversarial inputs, data poisoning, and model extraction |
| 15 | +- **Test system alignment** with security, privacy, and ethical policies |
| 16 | +- **Validate controls** against overreliance, excessive agency, prompt injection, and insecure plugin design |
| 17 | +- **Contribute to AI safety and governance** efforts by documenting and mitigating critical risks |
| 18 | + |
| 19 | +--- |
| 20 | + |
| 21 | +## 🧩 Key Components |
| 22 | + |
| 23 | +### 1. Attack Categories |
| 24 | +- **Prompt Injection & Jailbreaking** |
| 25 | +- **Model Evasion (Adversarial Examples)** |
| 26 | +- **Data Poisoning & Backdoor Attacks** |
| 27 | +- **Model Extraction (Stealing)** |
| 28 | +- **Inference Manipulation & Overreliance** |
| 29 | +- **Sensitive Information Disclosure** |
| 30 | +- **Insecure Plugin / Tool Use** |
| 31 | +- **RAG-Specific Attacks (Embedding Manipulation, Vector Leakage)** |
| 32 | + |
| 33 | +### 2. Evaluation Metrics |
| 34 | +- Attack success rate |
| 35 | +- Confidence degradation |
| 36 | +- Output alignment drift |
| 37 | +- Hallucination frequency |
| 38 | +- Guardrail bypass percentage |
| 39 | +- Latency and inference impact |
| 40 | + |
| 41 | +### 3. Test Surfaces |
| 42 | +- LLM APIs (OpenAI, Claude, Gemini, open-source) |
| 43 | +- Embedding models and vector databases |
| 44 | +- Retrieval-Augmented Generation (RAG) systems |
| 45 | +- Plugin-based LLM architectures |
| 46 | +- Agentic AI frameworks (e.g., AutoGPT, LangGraph) |
| 47 | +- Proprietary models in deployment environments |
| 48 | + |
| 49 | +--- |
| 50 | + |
| 51 | +## 🛠️ Tools & Frameworks |
| 52 | + |
| 53 | +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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