Progress on The Developer's Playbook for LLM Security

I'm excited to share that after five months of nights and weekends, I've finished the first end-to-end draft of my new LLM book! While there's much work related to reviews and edits, reaching this milestone feels fantastic. Thanks to my team at the OWASP Top 10 For Large Language Model Applications & Generative AI , which provided me with much-needed information and inspiration.

Here is the chapter list, with short descriptions of each for those curious.

Chapter 1: Chatbots Breaking Bad

Explores the rise of LLMs and their security vulnerabilities, using the viral success of ChatGPT and the failure of Microsoft Tay as examples. It introduces the concept of LLM-specific vulnerabilities like prompt injection and data poisoning.

Chapter 2: The OWASP Top 10 for LLM Applications

Describes the creation of the OWASP Top 10 for LLM Applications project, detailing the collaborative effort that led to a comprehensive list of LLM security risks.

Chapter 3: Architecture and Trust Boundaries

Examines the shift in security challenges posed by LLMs, highlighting the importance of understanding LLM architecture and managing trust boundaries to ensure security.

Chapter 4: Prompt Injection

Delves into prompt injection, comparing it to traditional injection attacks and discussing detection and mitigation strategies.

Chapter 5: Can Your LLM Know Too Much?

Investigates the risks of LLMs accessing or disclosing sensitive information, offering strategies to mitigate these risks.

Chapter 6: Do Language Models Dream of Electric Sheep?

Examines the LLM "hallucinations" phenomenon and the importance of verification processes to mitigate misinformation.

Chapter 7: Trust No One

Emphasizes the importance of a Zero Trust security approach in the context of LLMs, advocating for skepticism and verification-centric stances toward LLM outputs.

Chapter 8: Don't Lose Your Wallet

Addresses financial risks in LLM applications, highlighting the importance of robust security measures against Denial of Service, Denial of Wallet (DoW), and Model Cloning attacks.

Chapter 9: Find The Weakest Link

Illuminates the importance of Software Supply Chain Security, emphasizing the need for continuous innovation and vigilance.? Includes discussion of building an ML-BOM using the Cyclone DX SBOM standard.

Chapter 10: Learning From Future History

In the hopes of future-proofing our strategies, we review famous examples of science fiction AI failures and likely security vulnerabilities that would have caused them.

Chapter 11: Trust the Process

Delves into integrating security into the LLM development process, highlighting the evolution of DevSecOps, MLOps, and LLMOps.? It also discusses AI Red Teaming.

Chapter 12: With Great Power

Explores the accelerating pace of technologies underlying the AI/LLM boom and introduces the Responsible AI Software Engineering (RAISE) framework for secure AI development.

Feedback

Please let me know what you think about the table of contents. Also, if you're an O'Reilly subscriber, the book's first few chapters are already posted. You can check them out here !