Prompt Engineering & LLM Security

Prompt engineering covers two intersecting angles: crafting effective prompts to steer LLM behavior, and understanding how prompts can be exploited as an attack surface.

Scope

Angle	Focus
Engineering	Writing clear, effective prompts that reliably steer LLM behavior
Security	How prompts can be abused — prompt injection, sensitive data disclosure

Key Concepts

• What prompt engineering is and why it matters
• Best practices: clarity, context/constraints, experimentation
• OWASP LLM Top 10 (2025) — specifically LLM01: Prompt Injection and LLM02: Sensitive Information Disclosure
• Google SAIF (Secure AI Framework) — broader guidance on secure AI systems
• Attack techniques for prompt injection and information disclosure

What to Ingest Next

1. Prompt injection attack techniques — hands-on coverage of LLM01:2025
2. Sensitive information disclosure techniques — hands-on coverage of LLM02:2025
3. Fundamentals of AI — foundational LLM concepts
4. OWASP LLM Top 10 for LLM Applications 2025 — full framework (https://genaisecurityproject.com/resource/owasp-top-10-for-llm-applications-2025/)
5. Google SAIF risks — full risk breakdown (https://saif.google/secure-ai-framework/risks)

See Also

• prompt-engineering — best practices for writing effective prompts
• prompt-injection — LLM01:2025 and how prompt injection works
• owasp-llm-top-10 — the OWASP framework for LLM security
• google-saif — Google’s Secure AI Framework