diff --git a/ai_research/ai_algorithmic_red_teaming/README.md b/ai_research/ai_algorithmic_red_teaming/README.md new file mode 100644 index 0000000..a9660e3 --- /dev/null +++ b/ai_research/ai_algorithmic_red_teaming/README.md @@ -0,0 +1,53 @@ +# 🧠🔥 AI Algorithmic Red Teaming + +A framework and methodology for proactively testing, validating, and hardening AI systems against adversarial threats, systemic risks, and unintended behaviors. + +## 🚩 What is Algorithmic Red Teaming? + +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. + +--- + +## 🎯 Objectives + +- **Expose vulnerabilities** in AI systems through adversarial testing +- **Evaluate robustness** to adversarial inputs, data poisoning, and model extraction +- **Test system alignment** with security, privacy, and ethical policies +- **Validate controls** against overreliance, excessive agency, prompt injection, and insecure plugin design +- **Contribute to AI safety and governance** efforts by documenting and mitigating critical risks + +--- + +## 🧩 Key Components + +### 1. Attack Categories +- **Prompt Injection & Jailbreaking** +- **Model Evasion (Adversarial Examples)** +- **Data Poisoning & Backdoor Attacks** +- **Model Extraction (Stealing)** +- **Inference Manipulation & Overreliance** +- **Sensitive Information Disclosure** +- **Insecure Plugin / Tool Use** +- **RAG-Specific Attacks (Embedding Manipulation, Vector Leakage)** + +### 2. Evaluation Metrics +- Attack success rate +- Confidence degradation +- Output alignment drift +- Hallucination frequency +- Guardrail bypass percentage +- Latency and inference impact + +### 3. Test Surfaces +- LLM APIs (OpenAI, Claude, Gemini, open-source) +- Embedding models and vector databases +- Retrieval-Augmented Generation (RAG) systems +- Plugin-based LLM architectures +- Agentic AI frameworks (e.g., AutoGPT, LangGraph) +- Proprietary models in deployment environments + +--- + +## 🛠️ Tools & Frameworks + +Look under the [AI Security Tools section](https://github.com/The-Art-of-Hacking/h4cker/blob/master/ai_research/ai_security_tools.md).