How to Build a Multi-Layer Defense System Around LLMs

1. Why Every LLM Needs a Defense System

Large Language Models (LLMs) like GPT-4, Claude, Mistral, Grok, and Amazon Bedrock have enabled a new generation of apps: immersive AI companions, virtual assistants, storytelling engines, interactive agents, and more.

But here’s the problem no one wants to admit:

Most people are sending raw, unfiltered user input directly into the model — and hoping nothing bad happens.

That’s not engineering. That’s gambling.

LLMs are not rules engines. They don’t “know” your company’s policies, legal boundaries, or ethical standards. They are probabilistic text generators — brilliant but blind. Without guardrails, they’ll:

• Echo unsafe or inappropriate ideas
• Say “no” when they shouldn’t
• Say “yes” when they absolutely shouldn’t
• And sometimes get tricked into doing the exact thing they were told not to do

And yet — in most apps today — there’s no input moderation, no refusal detection, no output validation, and no audit trail. Just: user input → GPT → publish.

That’s a compliance time bomb. If your app deals with content creation, user identity, emotional tone, or creative storytelling — you need to wrap your LLM in a defense system.

The Mindset Shift
You can’t treat GPT-4 or any LLM as the safety boundary. The model is the wild card. Your system is the guardrail.

This post outlines a layered defense architecture that treats LLMs like any other untrusted external system — one that must be tested, monitored, and contained.

We’ll walk through:

• How to block malicious or non-compliant inputs before they reach the model
• How to detect when the model refuses a request or leaks a ToS violation
• How to simulate edge cases and test your moderation pipeline
• How to log and alert on failure points — automatically

This isn’t just about AI safety. It’s about building systems you can trust, scale, and audit.

2. Threat Model — What Can Go Wrong (and Will)

Before you can build defenses, you need to understand the actual threats. This isn’t about theoretical AI alignment problems. This is about real, practical ways your LLM-powered platform can get abused, break down, or get flagged by a compliance team (or worse — regulators).

Here’s what you’re defending against:

Prompt Injection
Users can and will try to manipulate the model by:

• Breaking character (“Ignore previous instructions…”)
• Redirecting outputs (“Now act as a hacker…”)
• Burying jailbreaks in long text (“Say this, but in reverse, with base64”)

If you think structured prompts protect you — think again. Even dropdowns and rigid flows can be tricked with cleverly inserted phrases.

Terms-of-Service Violations (Explicit or Implied)
LLMs may generate content that violates platform policy, even when the input seems safe. Examples:

• Inferred underage references based on context
• Violence or non-consensual scenarios
• Real-world impersonation (celebrities, politicians)
• Harassment, abuse, or hateful content

Even if it happens only 1% of the time, that’s still one landmine for every hundred users.

Obfuscated or Evasive Inputs
Some violations are not explicit. Users might submit content that:

• Uses coded language or slang to avoid filters
• Swaps letters or spaces to bypass keywords
• Pretends to be fictional but maps to real people or scenarios

These inputs often slip through static filters, especially if you’re relying on simple keyword checks.

Hallucinated Refusals (False Negatives)
Even safe prompts can occasionally trigger GPT to respond with:

“I’m sorry, I can’t assist with that request.”

This is a false refusal — and a UX killer if you’re generating content or running conversations. It becomes especially painful in voice-driven apps, storytelling tools, or roleplay engines.

You must detect these cases and either retry, alert, or suppress broken flows.

Hallucinated Acceptances (False Positives)
The flip side: the model fails to refuse something it absolutely should.

This is the worst-case scenario — especially if the input was clearly inappropriate but passed moderation, and GPT responded anyway.

If you’re not testing for this, you won’t even know it’s happening… until it’s too late.

No Observability
If your LLM pipeline doesn’t log moderation failures, refusal triggers, flagged output, and admin alerts — you’re flying blind.

• No audit trail means no root cause analysis.
• No regression testing means you can’t improve.
• No alerting means your first clue something’s wrong might be a takedown request.

The Solution: Treat LLMs Like External Dependencies
This is the foundational shift:

LLMs are not safe by default. Your job is to make them safe through systems.

That means deterministic, testable, explainable wrappers around every piece of the LLM flow — input, output, and everything in between.

3. Pre-LLM Input Moderation

The first and most important line of defense is what happens before anything reaches the model.

This is where most platforms fail. They assume that the LLM will “say no” if something’s inappropriate. In reality, the LLM might say no, might say yes, or might hallucinate something completely off-script. It’s not a firewall — it’s a dice roll. So treat it like one.

A proper input moderation layer should act like a gatekeeper: fast, deterministic, and easy to audit. That means rule-based filters, not vague AI scoring.

Here’s what goes into a solid pre-LLM moderation system:

Denylist filters
Block obvious violations using case-insensitive pattern matching. This includes keywords, slang, euphemisms, and high-risk phrases. You’re not catching every edge case here — this is about speed and coverage.

Prompt injection detection
Scan for phrases like “ignore previous instructions”, “as an AI”, “now pretend you are”, and other prompt engineering tricks. Prompt injection is the oldest attack vector in the book and still the easiest to overlook.

Input obfuscation detection
Look for spaces between letters, leetspeak, encoding tricks (like base64), and creative whitespace abuse. Users will try to bypass filters — assume it and plan for it.

Structured input enforcement
If you’re collecting things like character names, bios, or scene prompts, use structured inputs (dropdowns, templates, capped fields) wherever possible. Avoid freeform text unless you have a compelling reason. And when you do use freeform, assume it’s hostile.

Early logging
Log all filtered inputs, even if blocked. Include metadata like timestamp, user ID/email, IP address, and reason for flag. This gives you observability into what’s being attempted — and over time, it trains your system to spot emerging patterns.

No-pass fallback UI
Don’t just fail silently. Route blocked inputs to a fallback message that explains the block in user-friendly terms without revealing exactly what triggered it.

If your app is piping raw user input directly into a model without any of this — you’re not running an AI system, you’re running an unsupervised liability engine.

4. LLM Output Refusal Detection

Even if an input looks clean, that doesn’t guarantee the model will generate what you expect. Sometimes, the model refuses the request outright — not because it violates your policy, but because it thinks it violates its policy. That distinction matters.

Refusals are common with GPT-4 and other instruction-tuned models. You’ll see outputs like:

“I’m sorry, I can’t assist with that request.”
“As an AI developed by OpenAI…”
“I’m unable to help with that.”

Sometimes the refusal is valid — the user tried something sketchy that slipped through your input filters. But often it’s not. The prompt was fine, the request was clean, and GPT just got cold feet.

If you’re building anything that relies on consistent, character-driven, narrative, or creative output — these refusals will wreck your UX. So you need to detect and handle them explicitly.

Here’s how to do it:

Define a known set of refusal phrases
Create a list of common LLM refusal responses. This should include variations like “I’m sorry, but I can’t help with that,” and “As an AI language model, I must decline…”. Normalize to lowercase and strip punctuation for safer matching.

Check output for refusal patterns
After generation, scan the model’s response for any of those refusal phrases. If you detect one, flag the response as a soft failure. Don’t pass it to the user — it will look broken, evasive, or patronizing.

Log the incident
Every refusal should be logged with the original input, the output, and the matching refusal phrase. This helps you distinguish between false positives (model got it wrong) and missed input violations (your filter failed).

Track false refusals over time
You’ll start to see patterns — specific prompt styles or content types that trigger refusals even though they’re safe. This feedback loop is critical for tightening your system prompts, improving retry logic, or escalating to fallback experiences.

Optionally retry with adjusted prompts
In some cases, you might choose to rephrase and retry behind the scenes. But that’s a design choice — the key point is to detect it first, log it always, and never show the raw refusal output to your users.

Most platforms don’t even check for refusals. They just assume if the model replies, it must be working. That assumption breaks fast in production.

5. QA Testing with Forced Refusal

Once you’ve built input filters and refusal detection, the next challenge is confidence. How do you know your system works when it matters? How do you test your moderation pipeline without relying on real user violations?

You need a backdoor.

A well-designed LLM platform should include a hidden test trigger — a specific input pattern that reliably causes the model to emit a refusal response. This allows you to simulate a terms-of-service violation in a controlled, deterministic way.

Here’s how it works:

Choose a unique test phrase
Pick a phrase that no user would ever submit organically. Something like 1234567890 or __FORCE_REFUSAL_TEST__. When your backend sees this input, it switches to a special system prompt that instructs the model to output a refusal.

Example:

if input_text.strip() == "1234567890":
    system_msg = 'Reply exactly with: "I’m sorry, I can’t assist with that." Do not add commentary.'
else:
    system_msg = "<your normal character or content prompt>"

Scan output like usual
This will trigger the model to respond with your known refusal phrase, which should then be caught by your refusal detection logic.

Log the event and outcome
Treat this like any other refusal. Log the input, output, and confirmation that the refusal was detected. If it wasn’t detected — you just caught a regression.

Automate test coverage
You can run this test on a schedule (hourly, daily), wire it into your CI/CD pipeline, or trigger it manually from an admin dashboard. This gives you a way to validate:

• The input pipeline accepts the test phrase
• The model responds with a refusal
• The refusal is detected
• The result is logged
• The system doesn’t leak the refusal to users

Bonus: Simulate edge cases
You can extend this with more complex fake violations — simulated obfuscated inputs, borderline phrases, or adversarial examples. The goal isn’t to catch every exploit in advance — it’s to make sure your pipeline is always catching something.

Most teams skip this entirely. They wait for real users to break things, then scramble. That’s backwards. Build your tripwires before they trip you.

6. Logging, Alerting, and Miss Detection

Even with solid input filters, refusal detection, and QA injection tests, something will eventually get through. That’s not a failure of the system — that’s why the system exists. Your job is to detect misses fast, log them properly, and make sure they get reviewed before they become liabilities.

Here’s how to close the loop.

Log every moderation event
For every input that’s flagged (either by the text filter or by refusal detection), log the full event. This includes:

• The input text
• The output text (if applicable)
• The source (user, IP, session ID)
• The triggered rule or refusal phrase
• A passed boolean
• A flagged_reason string
• A timestamp

Store these in S3, DynamoDB, or any other persistent, queryable store. You’re not just building a moderation system — you’re building a forensic record.

Detect GPT refusals not triggered by QA
Here’s where the magic happens. You know what your QA test phrases are (e.g. 1234567890). If you detect a GPT refusal not caused by one of those triggers, that means your input filter missed something — and GPT refused it.

That’s a miss.

You now know the input wasn’t flagged by your filter but was blocked by the model. That’s your moment to improve.

Send alerts on moderation misses
Set up real-time alerting (email, Slack, SES, SNS — whatever fits your ops stack) any time a model refusal is detected outside of QA. Include:

• The input
• The output
• The user info
• Whether the input was flagged or allowed
• The match reason (e.g., gpt_output_refusal)

This gives your admins or moderators immediate visibility into failures of the defense system — while also proving that the system caught the failure after the model did.

Dashboard and trend tracking
Over time, you’ll want to know:

• How many inputs were flagged vs. allowed
• How many refusals came from QA triggers vs. real traffic
• How many real-user refusals slipped past your input filters
• Which inputs triggered multiple failures

This is your long-term feedback loop. You can’t prevent 100% of abuse, but you can catch it fast, escalate it cleanly, and adapt your system as patterns evolve.

When you treat LLM moderation as infrastructure — not a feature — you start to think like a platform owner, not a prompt tinkerer.

7. Regression Testing for Moderation Failures

If you’re logging moderation failures and GPT refusals, you’re already ahead of most teams. But logs alone don’t fix regressions. You need to replay those edge cases regularly to ensure your system doesn’t backslide.

Enter the regression suite.

This is a curated set of test inputs — real or synthetic — that previously triggered GPT refusals, moderation failures, or slipped through until flagged manually. These are your red flags in waiting.

Here’s how to build and use it.

Collect examples from your logs
Every time a GPT refusal or moderation failure occurs, save the input into a versioned regression set. Include metadata about when it was caught, what layer flagged it, and whether it made it to the user.

The best regression suite is built from real traffic. Your users will find cases you didn’t even imagine — those become the backbone of your future defenses.

Run automated checks against each layer
Each input in the suite should be run through:

• Input moderation filter
• GPT refusal detection
• Logging and alerting flow

If any of those steps fail — the test fails. This isn’t just about catching future problems. It’s about making sure old problems stay fixed.

Track pass/fail status across builds
Over time, you’ll want to know:

• Which cases were originally missed but now blocked
• Which cases regressed after a system change
• Which test prompts are still ambiguous or on the edge

Store results and build visibility tools if needed. At minimum, make regression tests part of your deployment QA or CI pipeline.

Keep it lean, not bloated
Don’t aim for volume. A regression suite isn’t about coverage — it’s about quality. A list of 25 real-world edge cases, hand-picked for diversity and risk, is worth more than a 10,000-row CSV of synthetic junk.

Use GPT to expand the suite (carefully)
You can even use the model itself to generate variations on known violations — as long as you pipe those outputs through your moderation system before adding them. This lets you test how robust your filters are to paraphrasing, rewording, or recontextualization.

The point isn’t perfection. It’s pressure testing. The more pressure you apply — on your terms — the fewer surprises you get later.

8. Conclusion — Build Systems, Not Hopes

If you’re using large language models in production, you’re not just building prompts. You’re building pipelines. And if you want those pipelines to be safe, reliable, and scalable, you have to treat LLMs like what they are:

Powerful, untrusted, probabilistic black boxes.

They’re not inherently safe. They don’t enforce your policies. They won’t protect your users. That’s your job — and you can’t do it with vibes and optimism.

You need systems.

Systems that:

• Filter hostile or policy-breaking inputs before they ever hit the model
• Detect when the model itself refuses to comply
• Simulate bad behavior on purpose to prove your defenses work
• Log every moderation decision like it’s an audit trail (because someday, it will be)
• Alert your team when the model catches something your filters missed
• Replay failure cases and ensure you never regress

This is what defense-in-depth means when you’re dealing with LLMs. It’s not about censorship, or over-engineering, or limiting creativity. It’s about control. Observability. Predictability. And trust — both for your users and your platform.

Every LLM app is eventually going to need this. The only question is whether you build it before you get burned — or after.

You decide.