OWASP Top 10 for LLM Applications: AI Security Explained

Learn prompt injection fundamentals as the foundational vulnerability of LLM applications, covering direct, indirect, and multimodal attacks, real-world incidents, and defense strategies.

Analyze direct and indirect prompt injection techniques, including persona-based jailbreaks, token smuggling, many-shot conditioning, and poisoned contexts in web, PDF, email, and RAG pipelines.

Examine agentic prompt injection and multi-agent attacks that exploit LLMs' deputy role, exposing cross-agent vulnerabilities and worm propagation, and learn architectural mitigations like separation, least privilege, and human-in-the-loop controls.

Implement a defense-in-depth strategy against prompt injection with five layered controls—from input validation to operational monitoring—to reduce attack surface while enforcing structured outputs.

Explore how large language models reveal sensitive data like PII, PHI, and API keys and learn defense-in-depth strategies—data classification, access controls, and output monitoring—to prevent disclosures.

Explore prompt-based extraction attacks and 13 attack vectors in LLM deployments, including membership inference, training data reconstruction, context window probing, system prompt extraction, and rag poisoning.

Discover how the LLM supply chain creates security risk across seven trust boundaries, from pre-trained base models to third-party APIs and MLOps, with practical SBOM governance.

Map four stages of the llm supply chain, from data to deployment, and examine backdoors, data poisoning, plugin abuse, and dependency hijacking, with defense controls like provenance and hermetic builds.

Learn to defend the LLM supply chain by applying a four-stage framework—source and vet, verify and sign, deploy and isolate, and monitor and respond—plus cryptographic signing, SBOM, and continuous gates.

Learn how data poisoning and model poisoning embed malicious signals in training data and weights, spanning data collection to deployment, including rlhf and rag threats, with detection and remediation challenges.

Explore four attack vectors across the AI development pipeline, including poisoning attacks and backdoor triggers, RLHF manipulation, and misinformation risks, insider threats, and open data vulnerabilities.

explains how trust boundaries at model output enable exploitation through prompt injection, malicious output, and remote code execution. highlights validation, sanitization, and per-output defenses across html, sql, and shell contexts.

explores excessive agency in ai systems, defining agency, outlining risks of autonomous action, and applying controls such as least privilege, human oversight gates, and tool restrictions.

Explore excessive agency attack scenarios, detailing prompt injection, agent misinterpretation, and excessive action across AI agents, with defenses like least privilege, human in the loop, and action classification.

Apply least-privilege governance for AI agents using per-task scoping and just-in-time access. Enforce read, write, and execute tiers, implement human-in-the-loop gates, and audit with sandboxing and policy frameworks.

Learn extraction techniques and attack vectors to recover LLM system prompts, including direct requests, roleplay override, completion manipulation, translation and encoding, behavioral inference, and token probability analysis.

Understand how RAG security expands the attack surface by exposing a mutable vector store of embeddings, with injection and inversion risks across indexing, retrieval, and LLM output.

Explore how attackers exploit vector databases and embedding pipelines in RAG systems, mapping embedding poisoning, retrieval manipulation, index tampering, and data exfiltration through a detailed attack tree.

Learn to secure RAG pipelines by enforcing RBAC and namespacing at the vector DB layer, validating ingestion, enforcing integrity with SHA-256 hashes, and auditing multi-tenant isolation.

Explore how LLM misinformation and hallucinations create high-severity risks across domains. Learn root causes, impact domains, attack vectors, risk rating, grounding, and human-in-the-loop review.