Introduction to LLM - LLM Primer VII — AI Security
This page provides an easy-to-understand guide on LLMs (Large Language Models) from basics to applications for AI enthusiasts.
Chapter 17 — Future Threats and Emerging Defenses
Seventeenth post of the LLM Primer VII walkthrough — and the series finale. Agent risks and the lethal trifecta, multimodal attack surfaces, deepfakes and C2PA provenance, plus a closing map of the whole LLM Primer arc and the Physical AI sister volume.
2026-05-26
Chapter 16 — Secure Fine-Tuning and Adaptation
Sixteenth post of the LLM Primer VII walkthrough. Why fine-tuning aligned models degrades safety (Qi et al.), poisoned fine-tuning data, and rollback disciplines that keep the safety envelope intact.
2026-05-25
Chapter 15 — Building a Secure AI Organization
Fifteenth post of the LLM Primer VII walkthrough. Security culture for AI teams, red teams and internal audits, vendor risk (SOC 2, ISO 42001), and the emerging AI BOM.
2026-05-24
Chapter 14 — Bias, Fairness, and Responsible AI
Fourteenth post of the LLM Primer VII walkthrough. Sources of bias in LLMs, measurement (BBQ, BOLD, StereoSet, HELM), and the safety-utility trade-off honestly named.
2026-05-23
Chapter 13 — Regulatory Landscape
Thirteenth post of the LLM Primer VII walkthrough. The EU AI Act (Regulation 2024/1689), US EO 14179, Colorado AI Act, NIST AI RMF + GenAI Profile, and ISO/IEC 42001 as the compliance skeleton.
2026-05-22
Chapter 12 — Access Control and Identity
Twelfth post of the LLM Primer VII walkthrough. OAuth 2.0 + PKCE, ABAC vs ReBAC (Zanzibar), multi-tenant isolation, and token-bucket rate limits for LLM APIs.
2026-05-21
Chapter 11 — Observability, Logging, and Incident Response
Eleventh post of the LLM Primer VII walkthrough. Structured LLM logging with PII redaction, OpenTelemetry GenAI conventions, and the NIST SP 800-61 IR cycle adapted for probabilistic systems.
2026-05-20
Chapter 10 — Designing Secure LLM Architectures
Tenth post of the LLM Primer VII walkthrough. Isolation boundaries, policy engines (OPA, Cedar), microVM sandboxes, and the "lethal trifecta" of agent + private data + untrusted content.
2026-05-19
Chapter 9 — Model Integrity and Supply Chain Risks
Ninth post of the LLM Primer VII walkthrough. Open-source model dependency risk, Sleeper Agents (Hubinger et al.), safetensors vs pickle, CVE-2024-3568, and the SLSA / Sigstore artifact-signing discipline.
2026-05-18
Chapter 8 — Adversarial Attacks on Models
Eighth post of the LLM Primer VII walkthrough. Adversarial examples in NLP (HotFlip, TextFooler), model extraction (Tramèr et al., Carlini et al.), and the defensive strategies for API-boundary abuse.
2026-05-17
Chapter 7 — Hallucinations and Reliability
Seventh post of the LLM Primer VII walkthrough. Why hallucinations occur, the confidence-vs-correctness gap, and hybrid verification architectures — anchored by the Moffatt v Air Canada and Mata v Avianca cases.
2026-05-16
Chapter 6 — Retrieval-Augmented Generation Risks
Sixth post of the LLM Primer VII walkthrough. Trust boundaries in RAG, malicious document injection, PoisonedRAG and BadRAG, and monitoring retrieval flows for the attacker's fingerprints.
2026-05-15
Chapter 5 — Input Validation and Output Filtering
Fifth post of the LLM Primer VII walkthrough. Input sanitization, structured guardrails (NeMo, Llama Guard 3, Lakera, Bedrock), and red teaming with Garak, PyRIT, and promptfoo.
2026-05-14
Chapter 4 — Prompt Injection and Jailbreaks
Fourth post of the LLM Primer VII walkthrough. Prompt injection as a structural consequence, the jailbreak taxonomy (DAN, grandma, Zou et al. suffixes, Crescendo, Skeleton Key), and the four-layer mitigation matrix.
2026-05-13
Chapter 3 — Data Security and Privacy
Third post of the LLM Primer VII walkthrough. Training-data risks, memorization and extraction (Carlini et al., Nasr et al.), and the encryption, isolation, and retention disciplines that keep sensitive prompts contained.
2026-05-12
Chapter 2 — Threat Modeling for LLM Systems
Second post of the LLM Primer VII walkthrough. Adapting STRIDE, PASTA, and attack trees to LLM systems — model, prompt, data, and infrastructure as assets, and MITRE ATLAS as the LLM-specific adversary catalog.
2026-05-11
Chapter 1 — Why AI Security Is Different
First post of the LLM Primer VII walkthrough. Why LLM security is structurally different from traditional security — the collapsed code/data boundary, the probabilistic core, and the OWASP LLM Top 10 as a working checklist.
2026-05-10
LLM Primer VII — Series Introduction & Index
Kicking off the chapter-by-chapter walkthrough of Book VII in the LLM Primer series — AI Security. Why in LLM systems code and data are the same string, and the schedule for the seventeen posts that follow, May 10 through May 26. This is the series finale.
2026-05-09