The Future of AI Security: Why LLMs Need Extra Care

Generative AI (GenAI) is shaking up industries, offering businesses exciting ways to innovate and operate more efficiently. Tools like ChatGPT, Llama 2, and MidJourney allow companies to scale personalized customer experiences and streamline workflows like never before. But, as these technologies become more mainstream, they bring new challenges, particularly in security. 

With Large Language Models (LLMs) at the core of many GenAI solutions, it’s critical to understand the unique risks they pose and how to manage them effectively.

The LLM AI Cybersecurity & Governance Checklist, from the OWASP Top 10 for LLM Applications Team helps organizations do just that. It’s a practical guide designed to identify and mitigate the risks associated with deploying LLMs. While the checklist isn’t the focus here, it underscores an industry trend: LLMs require extra care when it comes to application security testing.

What Makes LLM Security So Unique?

LLMs are powerful, but they come with quirks that set them apart from traditional software. For one, they’re inherently unpredictable, meaning the same input can yield different outputs. While that’s part of what makes them so versatile, it also creates challenges in reliability and security.

For example, attackers can exploit vulnerabilities through techniques like prompt injection, where carefully crafted inputs force the model to produce unintended or harmful outputs. Another concern is data leakage, where sensitive information inadvertently surfaces in a model’s response. These risks are amplified when LLMs interact with plugins or APIs, making robust application security testing non-negotiable.

OWASP’s checklist emphasizes integrating GenAI security measures into existing governance frameworks. This includes regular vulnerability assessments with application security tools like HCL AppScan. In addition to testing underlying code, organizations need to review secure data handling practices, as well as testing their applications for weaknesses unique to LLMs, like their susceptibility to manipulation through semantic search.

Building Security into Your AI Strategy

One of the most important steps businesses can take is to treat LLMs as part of their broader security ecosystem, not standalone tools. This means folding LLM governance into existing protocols, from software reviews to data privacy measures. OWASP highlights the need for proactive security testing to uncover risks like insecure plugin designs or unauthorized access points that could lead to remote code execution.

Training is another key piece of the puzzle. Employees need to understand both the potential and the pitfalls of AI. Developers and cybersecurity teams, in particular, should be trained to recognize risks like deep fakes or impersonation threats. And don’t overlook the dangers of “Shadow AI”—when employees bypass approval processes to use unauthorized AI tools, creating vulnerabilities your team didn’t anticipate.

Why This Matters

Application security testing with tools such as HCL AppScan has always been critical, but the rise of GenAI is adding another layer of urgency. The risks tied to LLMs—like hallucinations (where models produce inaccurate or fabricated information) or adversarial attacks—aren’t just technical concerns. They can impact trust, compliance, and decision-making, with real consequences for your business.

By prioritizing security and governance, organizations can unlock the full potential of GenAI without exposing themselves to unnecessary risks. OWASP’s checklist offers a blueprint for doing just that, helping businesses innovate responsibly and confidently.

Generative AI isn’t just a trend—it’s the future. To stay ahead of emerging threats and make the most of everything AI has to offer, consider contacting the team at HCL AppScan for a fuller understanding of the tools and strategies you need to stay secure.