A recent McKinsey Global Survey on AI showed that currently, 65% of organisations regularly use generative artificial intelligence (GenAI) tools, showcasing its increasing importance in today’s digital landscape.
However, as businesses rely more on these tools, the security risks increase and the need for robust security measures becomes apparent.
LLMs vs GenAI: Understanding the Differences
Before we dive into GenAI security, let’s first discuss the difference between two common terms we often hear – LLMs and GenAI.
While they are closely related, these two serve distinct roles in the AI ecosystem. Understanding their differences and interconnections can help you leverage their capabilities effectively.
What Are LLMs?
Large Language Models (LLMs) are a subset of AI models specifically designed for processing and generating human-like text. Built using vast datasets and sophisticated algorithms, LLMs like OpenAI’s GPT have transformed how machines interact with human language.
Key Features of LLMs:
- Language Understanding: LLMs are designed to comprehend, process, and generate text that mimics human language.
- Pre-trained Models: These models undergo extensive training on diverse datasets, enabling them to understand context, syntax, and semantics.
- Applications
Language Translation
Sentiment Analysis
Text Summarisation
Chatbots and Conversational AI - Limitations:
They depend on the quality of the data they are trained on.
Prone to errors like hallucinations (producing false information).
What Is Generative AI?
Generative AI (GenAI) is a broader category of AI that focuses on creating new content, be it text, images, audio, or even videos.
While LLMs fall under the umbrella of GenAI, the term encompasses all models designed for generative tasks, including language.
Key Features of GenAI:
- Versatility: GenAI can generate not only text but also visuals (e.g., DALL·E, MidJourney), music, and other forms of media.
- Creative Output: It is designed to produce novel and contextually relevant content.
- Applications
Image and Video Generation
Music Composition
Synthetic Data Generation (for training AI models) - Limitations:
Ethical concerns, such as creating deepfakes or misinformation.
Dependency on large computing resources.
LLMs vs. GenAI: Key Differences
| Features | LLM | Gen AI |
|---|---|---|
| Scope | Focused on text processing and generation. | Broader, includes text, images, videos, etc. |
| Applications | Chatbots, translation, summarisation. | Creative content, media generation, synthetic data. |
| Capabilities | Text comprehension and synthesis. | Generation of novel and diverse content. |
| Dependency | Requires language-based datasets. | Uses multimodal datasets for diverse outputs. |
| Examples | GPT, Bard, Claude, LLaMA. | DALL·E, MidJourney, Synthesia, GPT-4 Vision. |
GenAI and Security
Attackers may exploit these tools without proper security, leading to data breaches or compromised outputs. This shows the importance of securing GenAI.
Generally, Large Language Models (LLM) models have safety and security measures embedded into the LLM model by AI model providers to allow the model to maintain ethical behaviour and safety standards.
However, these measures are insufficient. All models are susceptible to vulnerabilities such as jailbreaks, prompt injection attacks, and data leakage. According to Easy Jailbreak, LLM models have an average breach probability of around 60%.
GenAI Security and Safety Challenges
As GenAI applications grow, addressing security and safety issues becomes critical. Without proper safeguards, businesses risk exposing sensitive data and compromising user trust.
Security Issues
GenAI applications face several security challenges that need immediate attention:
- Prompt injection attacks
Malicious inputs can manipulate GenAI models, leading to inaccurate or harmful outputs.
- Sensitive data exposure
Improper handling of user inputs can leak intellectual property and personally identifiable information (PII).
- Inadequate content moderation
Unmonitored responses can lead to misinformation, hallucinations, or inappropriate content.
- Exploitation of vulnerabilities
Cyber attackers can exploit system flaws, leading to unauthorised access and potential data breaches.
Ensuring robust security measures can mitigate these risks and protect organisations and users.
Safety Issues
Safety concerns also arise when GenAI applications operate without proper oversight:
- Unreliable outputs
GenAI models may generate inaccurate or contextually irrelevant information without validation processes.
- Ethical concerns
AI systems can inadvertently produce biased or harmful content if safeguards are not in place.
- Uncontrolled access
Excessive usage without input limits can lead to operational slowdowns or resource overuse.
Addressing these safety challenges is crucial for maintaining trust and ensuring responsible GenAI deployment.
OWASP Top 10 for LLM Applications
GenAI applications face several common security risks. Understanding these risks is essential for protecting sensitive data and ensuring safe operations. The OWASP Top 10 identifies the key security challenges for GenAI systems, including:
- Prompt Injection: Attackers inject harmful commands to manipulate model outputs.
- Data Leakage: Sensitive information can be unintentionally revealed through model responses.
- Training Data Poisoning: Compromised training data can produce biased or unsafe outputs.
- Inadequate Access Controls: Weak access restrictions can allow unauthorised users to exploit systems.
- Malicious Output Generation: Models can be manipulated to produce harmful or unethical content.
- Model Theft: Attackers can extract and replicate model intellectual property.
- Output Hallucination: Incorrect or fabricated data can mislead users.
- Excessive Resource Use: Unrestricted inputs can lead to system overloads or operational delays.
- Cross-Site Scripting (XSS): Inputs can be exploited to inject malicious code into applications.
- Misaligned System Instructions: Vulnerabilities in model alignment can lead to unintended behaviours.
Addressing these risks with a comprehensive GenAI firewall helps protect applications from evolving threats and ensures secure, reliable operations.
How CloudsineAI Can Help
Securing your GenAI applications requires robust and tailored solutions. Cloudsine’s WebOrion® Protector Plus is a purpose-built GenAI firewall to protect your GenAI apps.
Key Highlights:
- Prompt Injection Attack Protection: WebOrion® Protector Plus is purpose-built to inspect and block sophisticated prompt injection attacks, including jailbreaking, DAN, and direct and indirect prompt injection attacks.
- Prevent Sensitive Data Leakage: WebOrion® Protector Plus can be programmed to check for sensitive Personal Identifiable Information (PII) including credit card info, bank account info, address, NRIC names, etc. This can be applied to both input prompts and output responses.
- Output Response Handling: WebOrion® Protector Plus can be configured to check the output response from the LLM or GenAI models. The output response checks can detect sensitive PII, improper output handling and system prompt leakage.
- Shieldprompt™ Add-on: For mission-critical security users, our unique Shieldprompt™ provides an advanced level of protection to include canary check, retokenisation, contextualised guardrails and vector database checks.
Schedule a demo today to explore how WebOrion® Protector Plus can secure your GenAI systems effectively.
Conclusion
The rise of GenAI technology has brought remarkable advancements, but it also demands robust security measures to address growing risks. A GenAI firewall like CloudsineAI’s WebOrion® Protector Plus provides a comprehensive defence to secure your GenAI applications and sensitive data.
Whether you’re developing chatbots, automated text generation tools, or retrieval-augmented generation applications, this solution ensures your operations remain secure and compliant.
Safeguard your organisation from threats while enabling GenAI applications to reach their full potential. Schedule a demo today.
