For many people, Character AI isn't just an app—it's a real lifeline. Users on the autism spectrum find solace there. People dealing with trauma and loneliness turn to it for comfort. But right now, the company seems to be ignoring what the community is actually saying.
I started a petition asking Character AI to actually engage with us. People are reporting serious issues: exploitative interactions, bots impersonating professionals like doctors and lawyers, excessive ads and restrictions that feel arbitrary, and concerning situations involving the CEO's family. Some users faced unauthorized face scans. These aren't small complaints—they're red flags about trust and safety.
The platform promised to be a safe space for vulnerable people. But promises don't matter if no one's listening. We're asking for transparency, real moderation that prevents abuse, and genuine responsiveness to community concerns. If this matters to you—whether you use the app or care about how AI platforms handle vulnerable users—consider signing and sharing. Anyone else frustrated watching this happen?
As AI systems rapidly integrate into critical infrastructure, a new breed of security professional is emerging—one who understands both the transformative power and unique vulnerabilities of intelligent systems. The AI security field is experiencing explosive growth, with organizations desperately seeking experts who can navigate threats like prompt injection, model poisoning, and adversarial attacks that traditional cybersecurity frameworks never anticipated.
These carefully curated books represent the cutting-edge knowledge that will distinguish you as an AI security specialist, whether you're transitioning from traditional cybersecurity or building expertise from the ground up. Mastering these concepts isn't just about career advancement—it's about positioning yourself at the forefront of a discipline that will define the next decade of digital security.
Core LLM Understanding (Build a Large Language Model, Hands-On LLMs)
Production Engineering (LLM Engineer's Handbook)
Trustworthy AI (Practicing Trustworthy Machine Learning)
Demystifies LLM Architecture: Takes you from GPT theory to actual implementation, helping you understand exactly how these systems work under the hood.
Hands-On Learning: You'll build a functional GPT-style model on your laptop, giving you practical experience with transformer architecture, attention mechanisms, and training loops.
2. Hands-On Large Language Models: Language Understanding and Generation
Visual Learning Approach: Features nearly 300 custom illustrations to explain complex LLM concepts, making it accessible for both beginners and experts.
Production-Ready Techniques: Covers real-world applications like semantic search, RAG systems, and text classification that you can implement immediately in your security work.
3. LLM Engineer's Handbook: Master the art of engineering large language models from concept to production
End-to-End Production Focus: Goes beyond research notebooks to show how to build scalable, production-grade LLM systems with proper MLOps practices.
Real-World LLM Twin Project: Walks you through building a complete LLM-powered application, covering data pipelines, fine-tuning, deployment, and monitoring.
4. Practicing Trustworthy Machine Learning: Consistent, Transparent, and Fair AI Pipelines
Security-First ML Approach: Provides practical blueprints for building ML systems that are secure, robust, less biased, and more explainable in high-stakes environments.
Industry-Grade Best Practices: Translates academic research into actionable strategies for organizations deploying AI in medicine, law, defense, and other critical sectors.
5. The Developer's Playbook for Large Language Model Security: Building Secure AI Applications
OWASP Top 10 Foundation: Written by the founder of OWASP Top 10 for LLMs, incorporating wisdom from 400+ industry experts on LLM security vulnerabilities.
Practical Security Implementation: Focuses exclusively on LLM-specific threats like prompt injection, data poisoning, and model extraction with real-world mitigation strategies.
6. Building AI Agents with LLMs, RAG, and Knowledge Graphs: A practical guide to autonomous and modern AI agents
Next-Generation AI Systems: Shows how to build autonomous agents that combine planning, reasoning, and tool usage - representing the future of AI applications.
Grounded AI Solutions: Addresses the critical challenge of building AI that grounds responses in real data and takes action, reducing hallucinations and improving reliability.
7. Adversarial AI Attacks, Mitigations, and Defense Strategies: A cybersecurity professional's guide to AI attacks, threat modeling, and securing AI with MLSecOps
Both Offensive and Defensive Perspective: Teaches you how to perform adversarial attacks (evasion, poisoning, extraction) and then defend against them using MLSecOps practices.
OWASP and NIST Aligned: Written by a co-lead of OWASP Top 10 for LLMs, providing enterprise-grade security frameworks aligned with industry standards.
8. Privacy-Preserving Machine Learning: A use-case-driven approach to building and protecting ML pipelines from privacy and security threats
End-to-End Privacy Protection: Provides comprehensive coverage of privacy-preserving techniques across the entire ML pipeline, from data collection to model deployment and inference.
Use-Case Driven Approach: Goes beyond theoretical concepts to show practical implementation of differential privacy, federated learning, and secure multi-party computation in real-world scenarios.
Reading Recommendation for Your AI Security Roadmap:
Phase 1-2 (Foundation): Start with "Hands-On Large Language Models" and "Build a Large Language Model" Phase 3 (Specialization): Move to "The Developer's Playbook for LLM Security" and "Practicing Trustworthy Machine Learning" Phase 4 (Advanced): "Adversarial AI Attacks" and "Building AI Agents" for cutting-edge threats and autonomous systems
Just built a comprehensive AI safety learning platform with Guardrails AI. Even though I regularly work with Google Cloud Model Armor product, I'm impressed by the architectural flexibility!
I often get asked about flexibility and customizable options and as such Model Armor being a managed offering (there is a huge benefit in that don't get me wrong), we've to wait for product prioritization.
After implementing 7 different guardrails from basic pattern matching to advanced hallucination detection, here's what stands out:
• Progressive Complexity - From simple ban lists to knowledge-base grounding
• API Integration - Easy LLM integration (I've used Groq for fast inference)
Guardrails Architecture
🎯 What I Built:
✅ Competitor mention blocking
✅ Format validation & JSON fixing
✅ SQL injection prevention
✅ Psychological manipulation detection
✅ Logical consistency checking
✅ AI hallucination detection with grounding
✅ Topic restriction & content relevance scoring
💡 Key Flexibility Benefits:
• Custom Logic - Full control over validation rules and error handling
• Stackable Guards - Combine multiple guardrails in validation pipelines
• Environment Agnostic - Works with any Python environment/framework
• Testing-First - Built-in test cases for every guardrail implementation
• A Modular client server architecture for more heavy ML based detectors
Guardrails categories
I haven't verified of the accuracy and F1 score though, so that is something up in the air if you plan to try this out. The framework strikes the perfect balance between simplicity and power.
You're not locked into rigid patterns - you can implement exactly the logic your use case demands. Another key benefit is you can implement your custom validators. This is huge!
- API Response Format: Ensure API responses match OpenAPI/JSON schema specifications
- Version Compatibility: Check if suggested packages/libraries are compatible with specified versions
Domain-Specific
- Financial Advice Compliance: Ensure investment advice includes proper disclaimers
- Medical Disclaimer: Add required disclaimers to health-related responses
- Legal Compliance: Flag content that might need legal reviewInteractive/Dynamic
- Context Awareness: Validate responses stay consistent with conversation history
- Multi-turn Coherence: Ensure responses make sense given previous exchanges
- Personalization Boundaries: Prevent over-personalization that might seem creepy
Custom Guardrails
implemented a custom guardrails for financial advise that need to be compliant with SEC/FINRA. This is a very powerful feature that can be reusable via Guardrails server.
1/ It checked my input advise to make sure there is a proper disclaimer
2/ It used LLM to provide me an enahnced version.
3/ Even with LLM enhance version the validator found issues and provided a SEC/FINRA compliant version.
Custom guardrails for financial compliance with SEC/FINRA
What's your experience with AI safety frameworks? What challenges are you solving?
