Introduction to LLM Red Teaming
Chapter 1: Foundations of LLM Red Teaming
What is Red Teaming: A General Overview
Why Red Teaming is Essential for LLMs
LLM Vulnerabilities: An Introduction
The LLM Red Teaming Lifecycle
Roles and Responsibilities in an LLM Red Team
Setting Objectives and Scope for LLM Red Teaming
Understanding the Attacker's Mindset
Legal Frameworks and Responsible Disclosure Practices
Hands-on: Defining Scope for a Mock LLM Red Team Operation
Quiz for Chapter 1
Chapter 2: Understanding LLM Attack Surfaces
Prompt Injection: Direct and Indirect Techniques
Data Poisoning: Training Data and Fine-tuning Attacks
Model Evasion and Obfuscation Tactics
Jailbreaking and Role-Playing Attacks
Extracting Sensitive Information from LLMs
Denial of Service and Resource Exhaustion in LLMs
Over-reliance and Misinformation Generation
Identifying Attack Vectors in LLM APIs and Interfaces
Practice: Analyzing LLM APIs for Potential Weaknesses
Quiz for Chapter 2
Chapter 3: Core Red Teaming Techniques for LLMs
Manual Adversarial Prompt Crafting
Automated Prompt Generation and Fuzzing
Utilizing Open-Source Red Teaming Tools
Persona-Based Testing: Simulating Malicious Actors
Multi-Turn Conversation Attacks
Exploiting LLM Memory and Context Windows
Identifying Bias and Harmful Content Generation
Semantic Similarity for Evasion
Hands-on: Crafting Adversarial Prompts
Quiz for Chapter 3
Chapter 4: Advanced Evasion and Exfiltration Methods
Gradient-Based Attack Methods: An Overview
Transfer Attacks: Using Substitute Models
Membership Inference Attacks Against LLMs
Model Inversion and Stealing Techniques for LLMs
Bypassing Input Filters and Output Sanitizers
Chaining Multiple Attack Techniques
Low-Resource and Black-Box Attack Strategies
Practice: Simulating an Information Exfiltration Scenario
Quiz for Chapter 4
Chapter 5: Defenses and Mitigation Strategies for LLMs
Input Validation and Sanitization for LLMs
Output Filtering and Content Moderation
Adversarial Training and Fine-Tuning for Enhanced Security
Instruction Tuning for Safety Alignment
Model Monitoring and Anomaly Detection
Rate Limiting and Access Controls for LLM APIs
Techniques for Detecting Jailbreaks
Strengthening LLM System Defenses
Hands-on: Implementing a Simple Input Sanitizer
Quiz for Chapter 5
Chapter 6: Reporting, Documentation, and Remediation
Structuring a Red Team Report for LLMs
Clearly Communicating Findings and Risks
Prioritizing Vulnerabilities Based on Impact
Recommending Actionable Mitigation Steps
Working with Development Teams for Remediation
Retesting and Verifying Fixes
Documenting Red Teaming Procedures and Plays
Practice: Writing a Sample Vulnerability Report Section
Quiz for Chapter 6
Semantic Similarity for Evasion
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- Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks, Nils Reimers and Iryna Gurevych, 2019 Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing (EMNLP) DOI: 10.48550/arXiv.1908.10084 - Introduces a method for generating semantically meaningful sentence embeddings, which is directly relevant to how LLMs process and compare the meaning of different prompts and to the technique of 'exploiting the LLM's own semantic space.'
- Training language models to follow instructions with human feedback, Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, Ryan Lowe, 2022 arXiv preprint arXiv:2203.02155 DOI: 10.48550/arXiv.2203.02155 - Describes InstructGPT and the use of Reinforcement Learning from Human Feedback (RLHF) to align LLMs with human instructions and preferences, serving as a foundational reference for the advanced safety mechanisms that semantic evasion attempts to circumvent.