LLM Security: Risks, Best Practices, Solutions

Large language models (LLMs), such as ChatGPT, Claude, and Gemini, are transforming industries by enabling faster workflows, deeper insights, and smarter tools. Their capabilities are reshaping how we work, communicate, and innovate.

LLMs are an advanced form of artificial intelligence (AI) that are trained on high volumes of text data to learn patterns and connections between words and phrases. This allows them to generate remarkably human-like text and drive an increasingly wide range of use cases. But with this growing adoption comes responsibility. LLMs are now being used in everything from customer support to cybersecurity, which makes them high-value targets for misuse and attacks. If something goes wrong, the impact can be significant. Damaging outcomes can include sensitive data exposure, harmful or misleading content, and a loss of trust in AI systems.

For enterprises, the risks are even greater. A single data breach or misuse of an LLM can lead to data leaks, damage to brand reputation, or violations of privacy and compliance regulations—all of which carry high costs.

Then there are the blind spots. LLMs can sound confident while being completely wrong. Their outputs can reflect existing biases, miss cultural nuances or subtly shift how people make decisions. The more we rely on them, the easier it is to stop questioning what they say. That’s why it’s critical not just to build smarter models, but also to stay clear-eyed about what they might miss and what we might miss because of them.

In this blog post, we’ll examine the unique challenges of using LLMs. We’ll assess their security risks, explore some real-world examples of insecure use and describe how you can best protect your organization.

What makes LLMs a unique security challenge?

Traditional cybersecurity practices weren’t built with LLMs in mind. Unlike standard software systems, LLMs don’t produce predictable, rule-based outputs. They generate language: dynamic, often surprising, and sometimes risky. This opens a new category of vulnerabilities that businesses must now confront.

LLMs can inadvertently reveal sensitive data, behave unpredictably based on training inputs, or fall prey to novel forms of manipulation such as prompt injection. Because they don’t “think” in human terms, they can’t be patched or audited in the same way as other systems. The result? A fast-moving technology that’s powerful but also harder to govern.

Below, we break down the key challenges that make LLM security different and more complex than for traditional software applications.

• Dynamic outputs. LLMs generate unpredictable responses. They might expose sensitive or confidential information in user interactions, making it hard to control what is shared.
• Vast data ingestion. LLMs are trained on large datasets that might include sensitive or proprietary information. When models are customized for specific organizations, there is a risk that sensitive data becomes embedded in the models and leaked during use.
• Opaque decision-making. LLMs operate as "black boxes," making it difficult to understand how they generate certain outputs. This lack of transparency can complicate risk management and make it harder to spot potential issues.
• New attack surfaces. LLMs create new vulnerabilities beyond traditional web app or cloud security layers. These include prompt injection attacks and data poisoning. Models might leak sensitive information if not configured correctly.

These factors make LLMs a unique challenge. Organizations must implement strong security measures to ensure data protection and compliance.

Common LLM security risks to watch

LLMs are powerful tools. They help with customer service, speed up work, and support decision-making. But they also bring new security risks. Here are some to keep in mind:

• Prompt injection attacks. Crafty prompts can manipulate models to disclose sensitive information, bypassing traditional security measures.
• Data leakage. Poorly configured models might expose private data during user interactions.
• Model inversion attacks. Hackers might be able to guess what data a model was trained on. This can expose customer data, business plans, or other private content.
• Insecure third-party integrations. Many LLMs connect to outside tools such as APIs or plugins. If these aren’t secure, they can leak data or be used for attacks.
• Over-reliance and false or misleading outputs. Sometimes, LLMs make things up. If people implicitly trust model output without verifying it, it can lead to bad decisions or rule breaking.
• Denial-of-service (DoS) attacks via token abuse. Attackers can overload models by using long or repeated prompts. This can slow down or crash AI services.
• Phishing and social engineering at scale. LLMs can generate highly convincing messages that mimic legitimate communication. This makes it easier for attackers to craft and distribute targeted phishing campaigns or social engineering attacks. In turn, this increases the risks of credential theft and data breaches.
• Shadow AI usage. Workers might use public AI tools without permission. They might enter private data, putting their organizations at risk.

To use LLMs safely, companies need strong rules and smart tools. They must watch how models are used, train staff on safe use, and block risky behavior before it causes harm.

Real-world examples of LLM security incidents

LLM-related security issues are not just a future concern — they’re already happening. Let’s look at a few examples:

Samsung: Data leaks via ChatGPT

In 2023, engineers at Samsung used ChatGPT to help with tasks such as debugging code and summarizing notes. In the process, they entered confidential company data. This included source code and internal information.

Because ChatGPT stores user input to improve its performance (unless users opt out), confidential Samsung data might have been absorbed into the model. This raised serious concerns about leaking trade secrets or exposing company intellectual property (IP). After the incident, Samsung restricted the use of ChatGPT and began building its own AI tools for internal use.

Why it matters

The Samsung example didn’t arise from malicious actions, just routine attempts to speed up work. But without clear boundaries and understanding of how LLMs handle input, even everyday interactions can expose highly sensitive data.

DeepSeek AI: Privacy concerns

DeepSeek is a Chinese AI startup that developed DeepSeek-R1, a powerful and affordable language model like ChatGPT. DeepSeek-R1 is used for tasks such as writing, coding, and analyzing data, making advanced AI more accessible to businesses and developers. Due to its efficient design, it uses fewer computing resources, which helps lower costs.

However, its rapid growth has raised concerns around data privacy and security. According to its privacy policy, DeepSeek stores user data on servers in China, where it can potentially be accessed by the government[JB1] . This has sparked caution among organizations worried about sensitive information and regulatory compliance. [MD2] 

Chevrolet dealership: AI chatbot offers $76,000 car for $1

A Chevrolet dealership’s AI chatbot made headlines after it mistakenly offered a $76,000 SUV for just $1. A user chatting with the bot asked for a massive discount and the AI agreed. It went as far as saying, “That’s a deal.” The chatbot didn’t have guardrails in place to catch the error, so it responded as if the deal was real.

Screenshot of conversation with Chevrolet chatbot

While the dealership didn’t honor the price, the story spread quickly online and raised serious questions about using AI in customer service. It showed how easily LLMs can be manipulated and why companies need to set clear limits on what these bots can and can’t say.

Best practices for securing LLMs

To safely integrate LLMs into your workflows, we recommend following these key security practices:

• Enforce access controls. Use role-based permissions. Limit access to sensitive data sets to only those who absolutely need it.
• Filter inputs and outputs. Monitor for sensitive or harmful content to prevent data leaks and misuse. Analyze model responses to ensure they don’t reveal sensitive data. This is particularly important after updates or retraining cycles.
• Practice data minimization. Limit the data provided to LLMs strictly to what is required for their specific task.
• Fine tune with guardrails. Tailor LLMs with custom safety constraints aligned to your business needs.
• Perform regular security audits and penetration testing. Implement a program of regular audits and penetration testing that explicitly accounts for LLM risks and behaviors.
• Educate users on secure and responsible LLM use through ongoing training and clear guidelines.
• Monitor for shadow AI. Implement controls to identify and respond to unapproved LLM usage across the organization.

Solutions and tools to strengthen LLM security

Securing LLM use is essential. Organizations should consider a layered approach, leveraging specialized tools and practices to protect both the models and the data they process. Key solutions include:

• Prompt monitoring platforms. These tools help detect and flag unusual or malicious prompt behavior that might indicate prompt injection attacks or misuse. By continuously analyzing inputs and outputs, they provide early warning signs of potential threats. Organizations can write policies to detect or even prevent different prompts based on that categorization. This ensures that content generated by AI aligns with company goals. And it reduces the risk of security breaches.
• Data security for LLMs. Traditional DLP strategies must evolve to meet the unique challenges of AI environments. Modern data security tools need to incorporate adaptive capabilities and intelligent response mechanisms tailored for LLMs.
• AI security gateways. Acting as a checkpoint between users and the LLM, these gateways manage and filter traffic. They enforce security policies, authenticate users, and help prevent unauthorized or risky access to AI services.
• Zero trust architecture for AI systems. Applying zero trust principles to LLMs means assuming no implicit trust. Every request to access or interact with the model must be verified and continuously assessed. This approach is critical when LLMs handle sensitive tasks or data.
• Secure LLM APIs. When you’re integrating LLMs into applications, secure development practices are essential. This includes proper authentication, input validation, rate limiting, and API gateway management to reduce attack surfaces and prevent exploitation.

By combining these LLM security tools and practices, organizations can build a more resilient and secure AI environment that aligns with modern risk management standards.

Future outlook: Building LLM security into your AI strategy

LLMs are becoming a bigger part of how businesses operate and that trend isn’t slowing down. With more LLMs in play, security must keep up and scale right alongside them. Organizations that start building strong AI security programs now will be better prepared for whatever compliance rules come next. In the process, they’ll earn trust from customers and stay open to new ideas and innovation.

We’re also seeing LLMs becoming more specialized, designed for specific industries such as healthcare, finance, and legal. Plus, as LLMs get woven into everyday tools such as Google Workspace and Microsoft 365, they’ll become a natural part of how we work. But with that convenience comes new risks, so making security a priority is key.

At the end of the day, keeping your LLMs secure is just good business sense. It’s not just your technology at risk—it’s your whole business.

How Proofpoint can help

Proofpoint is uniquely positioned to help organizations navigate this evolving landscape. By harnessing advanced LLM capabilities, Proofpoint Data Security Posture Management and Proofpoint’s data loss prevention solutions protect sensitive information by stopping it from being uploaded, pasted, or entered into tools such as ChatGPT. With automatic permission controls, our solutions also limit access to shared files by enterprise copilots.

Additionally, Proofpoint tracks and oversees AI data flows across multiple cloud environments. It enforces data access rules and ensures consistent classification and risk management for LLMs.

With Proofpoint’s AI-powered security, your organization can confidently scale LLM adoption while maintaining strong defenses. To learn more, visit us at Proofpoint.com or contact us today.