Evaluating Open Source LLMs for Modern Cyber Defense
Like other popular enterprise AI products built on open LLMs, including Cursor, Windsurf, and Salesforce Agentforce, the integration of AI and machine learning into cybersecurity has become both an opportunity and a challenge. As more organizations turn to AI to bolster their defenses, adversarial use of these technologies has also increased. One of the most pressing areas of exploration is the use of large language models (LLMs) in cybersecurity. Specifically, we must consider which open LLMs are most effective not only for defense, but also for how they may be leveraged in cyber attacks. In this analysis, we examine the key factors that make an open LLM well suited for cybersecurity applications, while also considering the vulnerabilities that may be exploited by malicious actors.
Generalization and Flexibility: The Core of Capability
At the heart of any effective LLM is its ability to generalize across diverse scenarios. This capability underpins the model’s intelligence and allows it to adapt to a wide range of cybersecurity threats, whether known or emerging. When choosing an LLM for cybersecurity purposes, it is critical to consider the model’s base capabilities and how well it generalizes beyond narrow tasks.
Fine-Tuning and Customization: Crafting a Specialized Defense
The ability to fine-tune an open LLM is crucial for tailoring it to specific cybersecurity needs. Fine-tuning enables organizations to improve a model’s proficiency in recognizing, analyzing, and responding to threats. This flexibility is particularly important in cybersecurity, where the threat landscape is constantly shifting and frequent updates are required. A model that can be easily fine-tuned is more likely to remain relevant and effective as new attack patterns emerge.
Safety Mechanisms: Balancing Security and Vulnerability
Safety mechanisms embedded within LLMs are designed to prevent misuse. However, these controls can also hinder effectiveness in certain cybersecurity applications, particularly those involving automation. While a human may be able to prompt-engineer a one-off jailbreak to bypass safety settings, automated agents cannot rely on this approach. At the same time, adversaries are increasingly using automated AI agents in fast-moving swarm attacks. To match the speed of these threats, defenders must also rely on automated AI agents.
This reality makes many commercial closed AI models ineffective for certain defensive responses, as their safety censorship prevents automated execution of critical actions. These restrictions continue to tighten over time, further reducing the usefulness of large closed models in high-stakes cybersecurity work. In defensive contexts, it is often necessary to have access to the same classes of tools adversaries are using. Hackers, organized crime groups, and state actors are not constrained by AI safety norms or regulations. As a result, the small market of competent, uncensored open LLMs plays an essential role in effective cyber defense.
Model Size and Efficiency: The Cost-Effectiveness Equation
Model size is another critical consideration. Larger models are often more capable across a broad range of tasks, but they require significant computational resources, sophisticated data center infrastructure, and are difficult or impossible to operate privately. They also tend to generate tokens more slowly and are less practical for covert use by malicious actors.
Smaller LLMs, by contrast, are more portable and can be deployed across a wide variety of environments, including those with limited compute. This makes them well suited for cost-effective deployment at scale, including at the edge, and for running privately when performing sensitive cybersecurity work. Private operation allows organizations to analyze vulnerabilities without exposing findings to third-party LLM providers before fixes are implemented. These same characteristics also make smaller models attractive to adversaries, who value speed, efficiency, and the ability to run models stealthily in compromised environments. Small open models are already the backbone of large-scale AI attack swarms, and this trend is likely to accelerate.
Country of Origin: Fight Fire with Fire
Given these factors, adversaries will use the most effective LLMs they can acquire and adapt for their purposes. Today, China is the leading producer of open LLMs. If defenders want access to the same classes of tools their attackers are likely to use, they should strongly consider including Chinese open LLMs in their defensive toolkits.
While future open models from other countries may eventually rival or surpass current options, excluding Chinese models from a cybersecurity stack today creates blind spots in threat modeling and red team operations. Chinese models are trained on significantly more Chinese-language data than most alternatives, which can be a meaningful advantage when dealing with threats involving Chinese technology, software, or language.
The device you are reading this on likely contains components manufactured in China. The network infrastructure connecting it to the internet likely does as well. Many AI products used daily by staff already rely on Chinese-developed models, including popular coding assistants. Organizations are already surrounded by Chinese technology. Avoiding Chinese open LLMs in cybersecurity, especially when they can be run privately, reflects incomplete threat modeling. Operating these models in controlled environments minimizes concerns about origin, and adversaries have already demonstrated that this is neither complex nor theoretical.
In conclusion, selecting the right open LLM for cybersecurity requires a nuanced understanding of model capability, operational constraints, adversarial incentives, and defensive needs. By evaluating generalization, fine-tuning, safety mechanisms, model size, and country of origin, organizations can make more informed decisions about how to strengthen their security posture. As AI-driven threats continue to evolve rapidly, defenders must prioritize practical effectiveness over theoretical comfort to stay ahead of adversaries who are unconcerned with nuance or restraint.
