Cybersecurity Risks of In-House LLM Development: An OWASP Top 10 Perspective for Enterprise Protection Strategies

In recent years, it's become quite rare to find someone who doesn't use AI in their work. Many enterprises are now opting to build their own Large Language Models (LLMs). This approach not only allows them to tailor the models more closely to their specific business needs and improve accuracy, but it also helps reduce the risk of data breaches. However, developing LLMs in-house can still introduce new cybersecurity vulnerabilities.

OWASP Top 10 for LLMs

The non-profit organization OWASP (Open Worldwide Application Security Project) regularly publishes its Top 10 critical risks across various domains. Since 2023, LLMs have been incorporated, and the latest version of these critical risks includes:

• Prompt Injection: Attackers can use carefully crafted prompts to manipulate an LLM into generating unexpected responses or behaviors, enticing the model to leak sensitive information or influence the answers it provides.
• Sensitive Information Disclosure: LLMs might inadvertently leak confidential data in their responses, or users might unintentionally input sensitive information. This can lead to unauthorized data access, privacy breaches, security vulnerabilities, or the exposure of source code and training methods.
• Supply Chain Vulnerabilities: During the LLM development and deployment process, security vulnerabilities in used components, services, or datasets can compromise system integrity, leading to biased output results, security vulnerabilities, or system failures.
• Data and Model Poisoning: Attackers can manipulate the data used to train LLMs, affecting the model's learning during pre-training, fine-tuning, or embedding stages. This not only degrades the model's security and impacts accuracy but can also lead to biased or harmful content output, or even inject vulnerabilities, backdoors, or biases that only trigger under specific conditions.
• Improper Output Handling: If an LLM's output isn't properly validated, filtered, or processed before being displayed, it could lead to front-end attacks (like XSS or CSRF) or back-end attacks (like SSRF or RCE).
• Excessive Agency: When an LLM in an application is granted too much autonomy or excessive permissions, allowing it to execute actions via plugins or tools without sufficient control, it can lead to access or disclosure of confidential information, altered decisions, execution of unauthorized operations, or excessive resource consumption.
• System Prompt Leakage: When a model's internal instructions are leaked, attackers can leverage sensitive information, internal mechanisms, rules, or permissions to understand how the system operates, identify weaknesses, bypass controls, or launch further attacks.
• Vector and Embedding Weaknesses: This applies to LLM systems that use Retrieval Augmented Generation (RAG) techniques. Attackers can exploit vectors and embeddings to inject harmful content into the model, manipulate model output, or access sensitive information.
• Misinformation: LLMs can produce seemingly credible but actually incorrect or misleading information (i.e., "AI hallucination"). This can lead to cybersecurity issues (for example, recommending non-existent malicious software packages), reputational damage, and legal liabilities.
• Unbounded Consumption: Attackers can manipulate an LLM to generate a large volume of output or send numerous resource-intensive requests, leading to Denial-of-Service (DoS) attacks, degraded performance, additional costs, or attempts to illegally replicate the model through mass queries.

How to Approach LLM Security Protection

Just as many enterprises deploy firewalls and Web Application Firewalls (WAFs) for their internal networks, LLMs also have corresponding defense services. Master Concept's partner, Cloudflare, offers a notable service called Firewall for AI. This "AI Firewall" is specifically designed as a security layer to protect Large Language Models (LLMs). It can scan every prompt for patterns and characteristics of potential attacks before API requests even reach your model.

For example, addressing the risk of Prompt Injection from the OWASP Top 10 for LLM, the AI Firewall can analyze prompts, score them based on their malicious potential, and categorize content (e.g., offensive, religious, sexually suggestive, politically sensitive). This allows users to pre-configure rules to block or manage relevant requests, effectively preventing specially crafted inputs from causing the LLM to generate unintended responses. Furthermore, the AI Firewall can leverage Sensitive Data Detection (SDD) WAF managed rule sets to identify Personally Identifiable Information (PII) or confidential data (like credit card numbers or API keys) that the model might inadvertently output in its responses.

At its core, an AI Firewall is similar to a WAF, but it's specifically enhanced for the unique risks of LLMs, particularly in handling prompts and outputs. It protects LLMs through analysis, filtering, and restriction mechanisms.

Securing Your LLM from the Ground Up

When you're first building your own LLM, choosing the right platform can be a real headache for businesses. Google really focuses on version control and data governance; AWS offers high flexibility and tons of third-party integrations; and Azure specializes in seamless integration with the Microsoft ecosystem. On top of that, many major cybersecurity vendors are now leveraging their own resources to develop AI platforms that combine their inherent security foundations, which makes LLM protection much easier to integrate.

Take Cloudflare's Workers AI, for example, a partner of Master Concept. It uses Cloudflare's global GPU network to run AI models at the edge, delivering low-latency, high-performance AI services. Workers AI integrates with the "AI Firewall" we mentioned earlier, effectively preventing issues like prompt injection, abuse of computing resources, and sensitive data leaks. While benefiting from Cloudflare's native DDoS protection, it also emphasizes "privacy by default," clearly stating that it doesn't use customer data to train models and that the models don't learn from user behavior. Plus, it supports many popular open-source AI models.

Besides the AI firewall and infrastructure mentioned above, LLM protection can also start from access, endpoints, monitoring, and SIEM. Enterprises can implement the principle of least privilege, multi-factor authentication (MFA) and Passkey to ensure that only authorized users can perform specific operations; implement strict API controls, validate LLM's output content, to prevent data leakage and malicious code injection; through monitoring comprehensive system logs, behavioral patterns, and abnormal activities, to instantly detect potential threats; utilizing SIEM systems to integrate all information, conduct correlation analysis, identify more complex attack patterns, and support rapid incident response, etc. If you still don't know where to start, you are welcome to contact Master Concept's professional consultants!