Safe AI for Autonomous and Agentic Systems
Alignment, Robustness, and Mechanistic Interpretability
Preface
This book is a comprehensive playbook on Safe AI that includes AI alignment, machine learning security, adversarial robustness, mechanistic interpretability, and responsible deployment of modern intelligent and autonomous systems, including LLM-based agents and learning-based decision-making systems.
It is designed as a progressive guide, working equally well as a textbook from the beginner level to the advanced practitioner. The document will function as a combination of the research handbook, a structured learning tool, and a practical engineering guide.
The material covered includes academic literature, empirical case studies, and hands-on adversarial evaluations. The topics covered are the traditional adversarial methods of machine learning, the safety of large language models, agentive systems, and audits of the external behavior and internal mechanisms used in the models. Mechanistic interpretability is included as a first-class tool for alignment, evaluation, and monitoring alongside behavioral testing and adversarial stress analysis.
Chapters introduce the reader to concepts progressively, with later chapters being more complex. However, advanced concepts are annotated with references or appendices to allow the reader to grasp the concepts at their own pace. The book aims to help readers understand concepts that help in analyzing, stress-testing, or aligning AI with long-term horizons.
There are no formal prerequisites, other than basic programming proficiency. If you have prior experience with machine learning, optimization, or systems engineering, you can move quickly through the early chapters, which can be thought of as conceptual on-ramps. As you progress through the text, you will become familiar with:
- Statistical reasoning for uncertainty, robustness, and evaluation
- Algorithmic thinking for scalable and secure AI systems
- Sequential decision-making and reinforcement learning
- Adversarial machine learning
- evasion, poisoning, and backdoor attacks
- model extraction and inversion
- adversarial training and robustness analysis
- Mechanistic interpretability
- internal representations and features
- circuits and causal interventions
- representation stability under distribution shift
- Testing and evaluation
- black-box, grey-box, and white-box analysis
- adversarial probing and red-teaming
- safety benchmarks for LLMs and learning agents
- AI safety and alignment foundations
- prompt injection and instruction-following failures
- goal specification and misgeneralization
- oversight, monitoring, and agent control
- Research methodology
- experimental design
- reproducibility and benchmarking
- responsible evaluation of high-stakes AI systems
This book is intended for students, researchers, engineers, and practitioners seeking a structured and rigorous understanding of modern AI safety spanning foundational concepts through frontier challenges in alignment, interpretability, and autonomous systems safety.
Citation
Kumar, K. (2026). Safe AI for Intelligent and Autonomous Systems: Alignment, Robustness, and Mechanistic Interpretability..
Edition 2026-02.
License
This work is licensed under the MIT License.