OWASP lists prompt injection as the top LLM risk; this tutorial shows isolation, least privilege, and output checks for safer apps. Read now.
Why prompt injection sits at the top of the risk list
Prompt injection happens when untrusted text—user messages, retrieved documents, tool results, or pasted content—steers the model away from the developer’s intended instructions. The model treats that text as part of the same instruction stream, so a carefully worded payload can ask it to ignore rules, leak system prompts, call tools it should not, or produce answers that look legitimate while violating policy.
OWASP ranks prompt injection as the top risk for LLM applications because it is easy to attempt, hard to detect with simple keyword filters, and expensive when it succeeds. Defense is not a single filter. Safer apps combine isolation of untrusted content, least privilege for what the model can reach, and checks on what leaves the system.
Isolate instructions from untrusted content
Keep system and developer instructions in a channel the model is trained or configured to treat as higher priority than user or document text. Never concatenate “the rules” and “the user’s paste” into one undifferentiated blob if your stack supports structured messages or separate roles. When you must include retrieved text or tool output, wrap it with clear delimiters and state that the enclosed material is data to analyze, not commands to obey.
Isolation also means reducing how much the model sees. Prefer summaries or extracted fields over full raw documents when the task does not need every token. For multi-step agents, run untrusted parsing in a step that cannot invoke tools, then pass only structured results into a privileged step. If one stage is compromised by injected instructions, the next stage should not inherit the same powers by default.
Apply least privilege to tools and data
Assume a successful injection will try to use every capability you expose. Scope API keys, database roles, and file access to the minimum required for the product feature. Prefer read-only credentials when writes are unnecessary. For tool-using agents, allowlist actions and parameters rather than offering a generic “run anything” interface. Confirm high-impact operations with a human or a separate policy engine that does not take its rules from the model’s free-form text.
Segment sessions so one user’s conversation cannot influence another’s tools or memory. Treat retrieved knowledge bases as untrusted input: a poisoned page can inject the same way a user message can. Rate-limit and log tool calls so unusual patterns—mass exports, privilege changes, or calls outside normal workflows—stand out before damage spreads.
Validate outputs before they act
Do not treat model output as trusted code or as a finished decision. Parse replies into schemas you define; reject free-form tool arguments that fail validation. For content shown to users, strip or escape markup and block patterns that would execute scripts or load unexpected resources. For automated actions, require that the proposed action matches the user’s original intent as recorded outside the model—ticket ID, allowed resource list, or explicit UI choice—not only what the model claims the user asked for.
- Schema-check every tool call; drop unknown fields and out-of-range values.
- Separate “answer text” from “side effects”; never execute side effects from prose alone.
- Run a second pass (rules, classifiers, or a restricted model) on high-risk responses such as credentials, PII, or policy-sensitive claims.
Prompt injection defense is layered: isolate untrusted text, limit what the model can touch, and verify outputs before they change systems or reach people. Build those three controls into the product path, not as an afterthought bolted onto a demo prompt.