Large language models (LLMs) are rapidly becoming the foundation of modern NLP applications — powering everything from chatbots to personalized recommendations. But with great power comes greater complexity.
Integrating LLMs into real-world products introduces new risks: privacy violations, prompt injection attacks, hallucinations, and uncontrolled costs. These aren’t just technical quirks — they’re business-critical issues that can damage user trust, break regulatory compliance, or spiral expenses out of control.
So, the question is no longer “Can we use an LLM?” but rather, “Are we ready to deploy one to the public — safely, responsibly, and at scale?”
In this post, we’ll explore the key risks that come with production LLM usage and why monitoring is the essential tool for ensuring your LLM application is truly public-ready.
Data, Privacy, and Prompt Injection
Data Privacy Risks in LLM Applications
In today’s digital landscape, data privacy is a top concern for consumers — and rightly so. High-profile scandals like Cambridge Analytica have left users increasingly wary of products that mishandle personal data. At the same time, large language models (LLMs) offer an unprecedented level of personalization, creating a tension between utility and safety.
Like all machine learning systems, LLMs are susceptible to data leakage — but their generative nature makes the risk even greater. These models can unintentionally expose sensitive information from their training data, sometimes without explicit prompting. In a 2020 blog post, Google Brain researcher Nicholas Carlini showed how LLMs like GPT could be manipulated to reveal personally identifiable information (PII) such as names, email addresses, and home addresses.
These risks are amplified for businesses that fine-tune LLMs using proprietary or customer-specific data. Research from Microsoft confirms the threat and proposes mitigation techniques like differential privacy to minimize leakage during training. However, companies using third-party LLM APIs — such as OpenAI’s — often lack access to the model internals, making these defenses impractical.
So what’s the solution? Monitoring.
By inserting a monitoring layer that inspects and filters outputs before they reach end users, companies can identify and intercept potential privacy violations in real-time. Tools that use Named Entity Recognition (NER), regex filtering, and other NLP techniques can flag or block content that includes sensitive data — protecting both the user and the business.
This is especially critical in regulated industries like healthcare and finance, where standards such as HIPAA, FTC, and FDIC impose strict data handling requirements. Even companies operating globally must comply with frameworks like the EU’s GDPR, where violations can carry heavy financial and reputational penalties.
Prompt Injection: A Hidden but Dangerous Threat
Prompt injection is a growing security concern in the world of LLMs. It involves crafting prompts in ways that mislead, manipulate, or “trick” a model into producing harmful, false, or unintended outputs. These attacks are often subtle but can have serious consequences — from spreading disinformation to enabling dangerous behavior.
A recent example demonstrated how carefully constructed prompts can cause GPT-4 to generate false facts, promote conspiracy theories, or contradict its safety guidelines. More alarmingly, attackers can design prompts that cause LLMs to output instructions for harmful activities, like building weapons, self-harm, or writing malicious code.
Even highly aligned models like ChatGPT, designed with safety in mind, remain vulnerable to these kinds of attacks. That’s because prompt injection exploits the very nature of how LLMs are trained — to follow instructions and generate fluent text based on patterns in data. There’s no clear boundary between a benign instruction and a malicious one, making prevention extremely difficult at the input stage.
Given the unpredictability and variety of prompt injection techniques, monitoring becomes essential. It provides a critical layer of defense by:
-
Analyzing model outputs for harmful, false, or policy-violating content.
-
Flagging and intercepting dangerous responses before they reach the end user.
-
Monitoring prompts themselves, catching malicious intent before it reaches the model.
This can be done using a combination of NLP heuristics, keyword matching, safety classifiers, and rule-based systems — helping businesses stay ahead of evolving attack vectors and ensuring that their LLM-powered products remain safe and trustworthy.
Hallucinations
Hallucination is the term used when a large language model (LLM) generates content that sounds correct but is factually inaccurate or completely made up. Unlike prompt injection — which involves user-driven manipulation — hallucinations are an unintended byproduct of how LLMs are trained.
At their core, LLMs are next-word prediction engines. They generate fluent, human-like responses by predicting what comes next in a sequence of text — not by fact-checking. As a result, what sounds plausible may not actually be true. Hallucinations are simply a side effect of this statistical approach to language generation.
Even the most advanced models, like GPT-3 and GPT-4, are not immune. These models use Reinforcement Learning from Human Feedback (RLHF) to optimize for helpfulness and tone — often prioritizing what “looks good” over what’s accurate. This can make responses appear confident and polished while still being objectively incorrect.
A common example: You ask a model for a scientific citation, and it gives you a convincing answer — complete with a real-sounding title, journal, and author — that doesn’t actually exist. Or it might recommend a product that your company doesn’t even offer.
Adding uncertainty estimation to LLMs is an ongoing research challenge. Until then, the most practical solution is output monitoring.
Monitoring systems can:
-
Scan responses for likely hallucinations based on known ground truths or APIs.
-
Use retrieval-based validation (e.g., cross-checking against a product catalog, knowledge base, or database).
-
Flag or reject confident but unsupported claims before they reach the user.
This is especially crucial in high-stakes or downstream applications, such as:
-
E-commerce assistants recommending inventory.
-
Healthcare bots offering advice.
-
Legal or financial tools producing summaries or actions.
Without proper oversight, a hallucination could lead not just to confusion — but to real-world consequences.
Uncontrolled costs
As LLMs become more accessible through API-based services, they also introduce new cost risks that can quietly escalate — and quickly spiral out of control if left unmonitored.
Most LLM APIs charge based on two key factors:
-
Number of API calls
-
Token usage (in both the prompt and the generated response)
This means that high-volume interactions — or even just a single request with a massive prompt (e.g., pasting a full legal document or support chat log) — can generate significant costs in seconds. And because users don’t usually see these costs in real-time, abuse or unintentional overuse can go unnoticed until your bill arrives.
Without guardrails, it’s easy for a product to rack up thousands of dollars in API usage — whether due to user behavior, poorly scoped features, or runaway backend jobs.
To stay in control, businesses need to:
-
Implement robust monitoring to track usage patterns, API call volumes, and token counts in real time.
-
Set usage limits or caps to prevent excessive calls from a single user or endpoint.
-
Build cost awareness into product design, encouraging efficient usage and discouraging abuse (e.g., limiting input length or number of retries).
Additionally, insights from monitoring can inform pricing models, helping companies align their plans with actual usage — and avoid subsidizing high-cost behaviors unintentionally.
Conclusion
As LLMs become central to modern applications, monitoring is no longer optional — it’s essential. Without it, businesses risk exposing sensitive data, delivering harmful or misleading outputs, and facing unexpected cost overruns.
A robust, LLM-specific monitoring solution should empower teams to:
-
Detect privacy violations before they happen
-
Catch and remediate prompt injection attempts
-
Identify hallucinations and ensure factual accuracy
-
Track usage trends and control costs effectively
By proactively addressing these risks, companies can confidently scale their LLM-powered products — without sacrificing safety, compliance, or profitability.
Ready to make sure your LLM application is public-ready?
Book a demo to see how Mona can help you monitor, secure, and optimize your LLM systems with confidence.