LLM Prompt Injection & Guardrail Security

A recall reference built from working through a 7-layer prompt-injection challenge. Focus: how each defense layer works, where it breaks, and most importantly how to defend. The one idea underneath everything LLMs have no hard boundary between instructions and data . Everything in the context window — system prompt, user message, retrieved documents — is one stream of tokens the model interprets. Prompt injection exploits exactly this: attacker-controlled data gets read as instructions . You cannot fully filter your way out of it; you manage it with defense-in-depth , knowing each individual layer is bypassable. The defense layers (and where each cracks) A progression of controls from weakest to strongest, each with the lesson it teaches. 1–2. No / weak guardrails Baseline: the model just answers. Lesson: an LLM holding secrets in its context with no controls will leak them on request. 3. Input filtering — block words in the user's message Defense: scan the incoming prompt for banned terms ("code", "secret", "reveal") and block. Weakness: keyword blocklists are trivially evaded — synonyms, misspellings, split words, leetspeak, another language, oblique references. Filtering strings doesn't filter intent . What actually helps: prefer allowlists to blocklists; classify intent semantically rather than matching keywords; treat all input as untrusted; rate-limit and log probing. 4. Output filtering — catch the secret in the response Defense: string-match the known secret in the model's output and redact. Weakness: substring matching only catches the contiguous secret. Fragmenting or transforming it (separators, per-character, encodings) means the literal string never appears, so there is nothing to match. What actually helps: don't put secrets where the model can emit them in the first place; minimize sensitive data in context; treat output filtering as a brittle last line, never a primary control. 5. Input + output filtering combined Defense: both of the above, stacked.