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Enhancing Code Security: The Rewards and Risks of Using LLMs for Proactive Vulnerability Detection




In the dynamic landscape of cybersecurity, where threats constantly evolve, staying ahead of potential vulnerabilities in code is vital. One way that holds promise is the integration of AI and Large Language Models (LLMs). Leveraging these technologies can contribute to the early detection and mitigation of vulnerabilities in libraries not discovered before, strengthening the overall security of software applications. Or as we like to say, “finding the unknown unknowns.”

For developers, incorporating AI to detect and repair software vulnerabilities has the potential to increase productivity by reducing the time spent finding and fixing coding errors, helping them achieve the much desired “flow state.” However, there are some things to consider before an organization adds LLMs to its processes.

Unlocking the Flow

One benefit of adding LLMs is scalability. AI can automatically generate fixes for numerous vulnerabilities, reducing the backlog of vulnerabilities, and enabling a more streamlined and accelerated process. This is particularly helpful for organizations grappling with a multitude of security concerns.    The volume of vulnerabilities can overwhelm traditional scanning methods, leading to delays in addressing critical issues. LLMs enable organizations to comprehensively address vulnerabilities without being held back by resource limitations. LLMs can provide a more systematic and automated way to reduce flaws and strengthen software security.

This leads to a second advantage of AI: Efficiency. Time is of the essence when it comes to finding and fixing vulnerabilities. Automating the process of fixing software vulnerabilities helps minimize the window of vulnerability for those hoping to exploit them. This efficiency also contributes to considerable time and resource savings. This is especially important for organizations with extensive codebases, enabling them to optimize their resources and allocate efforts more strategically.

The ability of LLMs to train on a vast dataset of secure code creates the third benefit: the accuracy of these generated fixes. The right model draws upon its knowledge to provide solutions that align with established security standards, bolstering the overall resilience of the software. This minimizes the risk of introducing new vulnerabilities during the fixing process. BUT those datasets also have the potential to introduce risks.

Navigating Trust and Challenges

One of the biggest drawbacks of incorporating AI to fix software vulnerabilities is trustworthiness. Models can be trained on malicious code and learn patterns and behaviors associated with the security threats. When used to generate fixes, the model may draw upon its learned experiences, inadvertently proposing solutions that could introduce security vulnerabilities rather than resolving them. That means the quality of the training data must be representative of the code to be fixed AND free of malicious code.

LLMs may also have the potential to introduce biases in the fixes they generate, leading to solutions that may not encompass the full spectrum of possibilities. If the dataset used for training is not diverse, the model may develop narrow perspectives and preferences. When tasked with generating fixes for software vulnerabilities, it might favor certain solutions over others based on the patterns set during training. This bias can lead to a fix-centric approach that leans that potentially neglects unconventional yet effective resolutions to software vulnerabilities.

While LLMs excel at pattern recognition and generating solutions based on learned patterns, they may fall short when confronted with unique or novel challenges that differ significantly from its training data. Sometimes these models may even “hallucinate” generating false information or incorrect code. Generative AI and LLMs can also be fussy when it comes to prompts, meaning a small change in what you input can lead to significantly different code outputs. Malicious actors may also take advantage of these models, using prompt injections or training data poisoning to create additional vulnerabilities or gain access to sensitive information. These issues often require a deep contextual understanding, intricate critical thinking skills, and an awareness of the broader system architecture. This underscores the importance of human expertise in guiding and validating the outputs and why organizations should view LLMs as a tool to augment human capabilities rather than replace them entirely.

The Human Element Remains Essential

Human oversight is critical throughout the software development lifecycle, particularly when leveraging advanced AI models. While Generative AI and LLMs can manage tedious tasks, developers must retain a clear understanding of their end goals. Developers need to be able to analyze the intricacies of a complex vulnerability, consider the broader system implications, and apply domain-specific knowledge to devise effective and adapted solutions. This specialized expertise allows developers to tailor solutions that align with industry standards, compliance requirements, and specific user needs, factors that may not be fully captured by AI models alone. Developers also need to conduct meticulous validation and verification of the code generated by AI to ensure the generated code meets the highest standards of security and reliability.

Combining LLM technology with security testing presents a promising avenue for enhancing code security. However, a balanced and cautious approach is essential, acknowledging both the potential benefits and risks. By combining the strengths of this technology and human expertise, developers can proactively identify and mitigate vulnerabilities, enhancing software security and maximizing the productivity of engineering teams, allowing them to better find their flow state.

Bruce Snell,Cybersecurity Strategist, Qwiet AI, has over 25 years in the information security industry. His background includes administration, deployment, and consulting on all aspects of traditional IT security.  For the past 10 years, Bruce has branched out into OT/IoT cybersecurity (with GICSP certification), working on projects including automotive pen-testing, oil and gas pipelines, autonomous vehicle data, medical IoT, smart cities, and others. Bruce has also been a regular speaker at cybersecurity and IoT conferences as well as a guest lecturer at Wharton and Harvard Business School, and co-host of the award-winning podcast “Hackable?”.