LLM
Large language model
A neural network, usually a transformer, trained on large text corpora to predict the next token, which applications prompt to produce text, code or tool calls. It does not separate instructions from data, so anything placed in its context can steer it.
- Defined in
- OWASP Top 10 for LLM Applications 2026
Covered in
Contain prompt injection at the application boundary
Assume retrieved content can influence model output; enforce data access and consequential actions in components the model cannot authorize for itself.
Keep generated output out of executable contexts
Choose safe rendering and narrow operations so model text cannot become browser code, database syntax or an unrestricted command.
Worked threat model: an LLM agent with tools
A complete worked threat model for an LLM agent with tools: the data-flow diagram, the threats from prompt injection, excessive agency, data exfiltration, confused-deputy and memory poisoning, and the mitigations, mapped to the OWASP Top 10 for LLM Applications 2026 and the OWASP Top 10 for Agentic Applications 2026.
Red-team LLM applications and agents by their effects
A red-teaming method for LLM features and agents: scope the whole system, map attacks to OWASP and MITRE ATLAS identifiers, score trials from observed effects, report rates with confidence intervals and retest on every model or provider change.
Call a hosted LLM API safely
A code-first checklist for wiring up an OpenAI or Anthropic API call from a backend: keep keys server-side, redact PII before prompts and logs, choose provider retention settings deliberately, validate structured output and still treat it as untrusted, and set timeouts, retries and per-user cost caps.
Contain LLM misinformation (OWASP LLM07:2026)
Stop plausible but wrong model output from driving decisions and actions: ground answers in sources, verify every citation, separate generation from execution with a claim-check-act pattern, gate consequential actions on a human, and evaluate factuality on your own domain.
All 9 pages that cover LLM in the library
Practice: XSS lab: Render a model’s answer as text instead of active markup · Local project: Gate model and provider changes on a versioned red-team corpus · Local project: Run model-generated Python in a locked-down container