What Is LLM (Large Language Model) Security? | Starter Guide

Large language model (LLM) security is the practice of protecting large language models and the systems that use them from unauthorized access, misuse, and other forms of exploitation. It focuses on threats such as prompt injection, data leakage, and malicious outputs.

LLM security applies throughout the development, deployment, and operation of LLMs in real-world applications.

What is a large language model (LLM)?

A large language model (LLM) is a type of artificial intelligence that processes and generates human language. It learns from vast amounts of text data and uses statistical relationships between words to predict and generate responses.

LLMs are built using a deep learning technique called the transformer architecture.

In other words:

LLMs don't understand language the way humans do. But they're very good at modeling patterns in how we write and speak.

This allows them to perform tasks like answering questions, summarizing documents, and generating text across a wide range of topics.

LLMs are used in tools like chatbots, virtual assistants, and code generation platforms. Newer models are increasingly multimodal, meaning they work with images, audio, or video in addition to text.

Why is LLM security becoming such a major concern?

Large language models are now widely used in enterprise applications, customer service tools, and productivity platforms.

As adoption grows, so does the exposure to misuse, abuse, and unintended consequences. Prompt injection, sensitive data leakage, and unauthorized actions are no longer fringe cases. They're showing up in production systems.

Why?

Because LLMs are being embedded into real workflows. They generate customer responses. Write code. Handle internal documents. So the risks are no longer theoretical. They affect business operations, reputations, and legal standing.

The problem is, LLMs don't behave like traditional systems. They generate outputs based on probabilistic training. Not rules.

That makes their behavior hard to predict or constrain. Security tools built for static input/output logic often miss threats introduced by language-based interactions.

At the same time, deployment is easier than ever. That's lowered the barrier to adoption but raised the odds of unmanaged or poorly secured use.

And incidents are piling up. Samsung banned ChatGPT after an IP leak. A major airline faced legal consequences over false information from a chatbot.

These risks aren't going away. They're scaling.

What are the primary LLM risks and vulnerabilities?

LLMs introduce new security risks that go far beyond traditional software threats.

Again, these models aren't static applications. They're dynamic systems that respond to unpredictable inputs. Sometimes in unpredictable ways.

Which means:

Threats can show up at every stage of the LLM lifecycle. During training. At runtime. Even through seemingly harmless user prompts.

The OWASP Top 10 for LLM Applications is one of the first frameworks to map this evolving threat landscape. It outlines the most common and impactful vulnerability types specific to LLMs. It's modeled after the original OWASP Top 10 for web apps but focuses solely on language model behavior, usage, and deployment.

While not exhaustive, it provides a solid baseline to build from.

Important:

Many of these risks don't arise from bugs in the model itself. Instead, they stem from design decisions, poor controls, or failure to anticipate how users—or attackers—might interact with the system.

Here's a breakdown of the top LLM-specific risks to keep in mind, based generally (but not precisely) on OWASP guidance:

• Top GenAI Security Challenges: Risks, Issues, & Solutions
• What Is a Data Poisoning Attack? [Examples & Prevention]
• What Is a Prompt Injection Attack? [Examples & Prevention]

Real-world examples of LLM attacks

These aren't theoretical issues. They've already happened.

The following examples show how attackers have exploited large language models in the real world. Each case highlights a different type of vulnerability, from prompt injection to data poisoning.

Let's take a closer look.

Tay (2016)

Microsoft's Tay chatbot—while not technically an LLM by today's standards—was one of the earliest public examples of generative model misuse.

Tay learned from user input in real time. But within 24 hours, users flooded it with offensive prompts, which Tay began echoing.

The result: a massive reputational crisis and a shutdown. This was an early example of prompt injection and training-time contamination.

PoisonGPT (2023)

In a 2023 academic experiment, researchers from the University of Illinois Urbana-Champaign created a rogue LLM called PoisonGPT.

It looked like a normal model on Hugging Face but had been trained to output false facts.

In a real-world threat scenario, this tactic could poison open-source ecosystems and silently introduce misinformation into downstream applications.

Indirect prompt injection via web content (2023–24)

Security researchers have demonstrated how LLMs embedded in tools like note apps, email clients, and browsers can be manipulated by malicious content.

For example: An attacker sends a calendar invite with hidden instructions that trigger unintended actions in an LLM-powered assistant. The input looks safe to a user, but not to the model.

Jailbreak prompts and DAN-style attacks

Attackers craft prompts designed to bypass safety filters and make LLMs output restricted content.

Some impersonate alternate personas—like DAN (“do anything now”)—to trick the model into ignoring its guardrails. Others use reverse psychology or indirect phrasing to elicit banned responses.

While not linked to a single high-profile attack, jailbreak techniques have been widely demonstrated in both public tools like ChatGPT, Claude, etc.; and controlled environments. And they continue to evolve.

Training data leakage and memorization

LLMs can unintentionally memorize parts of their training data.

Researchers—like Nicholas Carlini and teams at Google DeepMind—have shown that models can regurgitate sensitive data, including names, emails, or private keys, when prompted in specific ways.

This becomes a serious risk when training data includes proprietary, user-generated, or unfiltered internet content. It's not guaranteed to happen, but it's been repeatedly demonstrated in lab settings.

How to implement LLM security in practice

LLM security doesn't come from a single product or fix. It's a layered approach. One that spans every stage of development and deployment.

The key is to reduce exposure across inputs, outputs, data, and model behavior.

Here's how to start.

Limit prompt exposure and control input surfaces

LLMs take input from many places. Browser plugins. Email clients. SaaS integrations. That makes it easy for attackers to slip in hidden prompts. Sometimes without the user even noticing.

So limit where prompts can come from. Filter inputs before they reach the model. Use allowlists and sanitation layers to block malformed or malicious text.

Isolate model execution

Don't run LLMs in the same environment as critical apps or sensitive data.

Instead, use containerization or function-level isolation to reduce blast radius. That way, if the model is tricked into calling an API or accessing data, the damage stays contained.

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• What Is Observability?

Monitor for jailbreaks and unusual responses

Some attacks look like normal queries. But the output isn't.

Watch for sudden changes in tone, formatting, or behavior. Log completions. Flag unusual results. Train your team to spot signs of a jailbreak or prompt injection in progress.

Restrict tools and capabilities

LLMs can be connected to powerful tools, like file systems, email accounts, or customer records. But that doesn't mean they should be.

Limit capabilities to only what's necessary. Set strict access controls around tool use and require user confirmation for sensitive actions.

Classify and validate model outputs

Treat every response from an LLM as untrusted by default.

Use classifiers to detect toxic, biased, or hallucinated content. Then pass results through validation layers, like rule checks or downstream filters, before delivering them to users or systems.

Secure the model supply chain

Start with the model itself.

Before deploying any LLM, open source or proprietary, validate its source and integrity.

Use cryptographic checksums, verified registries, and internal review processes. This helps prevent model tampering, substitution attacks, or unauthorized modifications.

Here's why that matters:

LLMs can be compromised before you even start using them. A poisoned or backdoored model might behave normally during testing but act maliciously in production. That's why secure sourcing is foundational to implementation.

Validate fine-tuning data before training

Fine-tuning makes models more useful. But it also opens the door to new risks.

So vet the data. Use automated scanners to check for toxic content, malicious payloads, and sensitive information. Then layer in human review for context and nuance.

Also: Preserve visibility into who contributed what and when. That auditability is key for tracing issues later.

Note: Even small amounts of bad data can introduce harmful behavior. Without guardrails, a fine-tuned model might ignore safety rules. Or behave in unpredictable ways.

Control data ingestion for RAG pipelines

Retrieval-augmented generation (RAG) adds dynamic context to model prompts. But it also introduces a new attack surface: untrusted retrieval sources.

To reduce risk, set up strict input validation and filtering on all retrieval data. That includes internal knowledge bases, document repositories, and third-party sources.

Also consider disabling natural language instructions in retrieved content, or wrapping them in trusted markup, so they can't hijack model behavior.

Treat RAG inputs with the same scrutiny you give prompts and training data.

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What makes LLM security different from traditional app / API security?

LLM security introduces new risks that aren't typically found in traditional application or API environments.

In traditional apps, the attack surface is more predictable. Inputs are structured. Data flows are well defined. And trust boundaries are usually static: between the front end, the API, and the database.

But LLM applications are different. Inputs are freeform. Outputs are probabilistic. And data flows in and out of the model from multiple sources, like APIs, databases, plugins, and user prompts.

Which means LLM apps require a different threat model.

They bring new trust boundaries that shift with each interaction. User prompts, plugin responses, and even training data can introduce vulnerabilities.

And since LLMs can't always explain how they arrived at an output, it's harder to validate whether security controls are working.

That means securing an LLM isn't just about hardening the model. It's about managing the whole system around it.

How does LLM security fit into your broader GenAI security strategy?

LLM security is one piece of the larger GenAI security puzzle. It deals specifically with securing large language models: their training data, inputs and outputs, and the infrastructure they run on.

Basically, it's the part of generative AI security that focuses on the model itself. That includes preventing prompt injection attacks, securing model access, and protecting sensitive data during inference.

But it doesn't stop there.

To build a complete GenAI security strategy, organizations have to combine LLM-specific protections with broader measures. Like governance, system hardening, data lifecycle security, and adversarial threat defense.

Ultimately, LLM security needs to integrate with the rest of your AI security controls. Not live in a silo.

That's the only way to ensure that risks tied to how the model is trained, used, and accessed are fully covered across the GenAI lifecycle.

LLM security FAQs