The Rise of Large Language Models

OpenAI has emerged as a pivotal force in the development of large language models (LLMs), positioning itself at the forefront of artificial intelligence research. Founded with the goal of ensuring that artificial general intelligence (AGI) benefits all of humanity, OpenAI has pursued a mission to leverage deep learning and vast datasets to create systems capable of solving complex human problems (OpenAI).

Its early work on foundational models like GPT-1 and GPT-2 laid the groundwork for subsequent advancements, culminating in the release of GPT-3. This model, with its unprecedented scale and training data, represents a significant leap in natural language processing (NLP) capabilities. The development of GPT-3 is part of a broader trajectory that OpenAI has described as a path toward AGI, emphasizing the potential of these systems to transform how humans interact with technology (OpenAI).

The YouTube video titled “A 1-hour general-audience introduction to Large Language Models” provides an accessible overview of the technical underpinnings of such systems, highlighting their ability to generate coherent text, answer questions, and perform tasks that were previously thought to require human-like understanding. These advances also carry serious ethical and security implications (The Alan Turing Institute).

The capabilities of GPT-3 have revolutionized the field of NLP by demonstrating the potential of large-scale language models to perform a wide array of tasks with minimal human intervention. With over 175 billion parameters, GPT-3’s training data encompasses an extensive corpus of text, enabling it to generate highly contextually relevant responses and adapt to diverse applications. This model’s ability to understand and produce human-like text has expanded the possibilities for automation, from customer service to content creation, while also challenging traditional paradigms in language processing.

The Medium article “The Journey of OpenAI GPT” further underscores how GPT-3 builds on earlier iterations, incorporating refinements in architecture and training methodologies to enhance its performance. By integrating vast amounts of data and refining its neural network structure, GPT-3 has achieved a level of fluency and versatility that sets it apart from its predecessors. This technological breakthrough has not only redefined the capabilities of NLP systems but also sparked debates about the broader implications of such models in society.

The sheer scale of GPT-3’s training data and its ability to generate text that mimics human cognition have made it a cornerstone in the evolution of AI, with applications spanning multiple industries (The Alan Turing Institute).

The potential for hostile actors to exploit GPT-3’s capabilities for disinformation and manipulation is a pressing concern. The model’s ability to generate convincing text at scale makes it a potent tool for spreading false narratives, creating deepfakes, and amplifying misleading content. The open-weight models developed by OpenAI, such as GPT-oss-120b and GPT-oss-20b, are accessible on platforms like Hugging Face, which raises questions about the security of these models when deployed in unregulated environments.

Hostile actors could leverage these models to craft sophisticated disinformation campaigns, tailoring content to specific audiences and bypassing traditional fact-checking mechanisms. The ease with which GPT-3 can be adapted to generate realistic text, combined with its capacity to mimic human writing styles, enables the creation of content that is difficult to distinguish from authentic sources. This poses a significant threat to public discourse, as misinformation can be disseminated rapidly and widely, undermining trust in institutions and distorting collective understanding.

The decentralized nature of these models further complicates efforts to monitor and mitigate their misuse, as they can be deployed by individuals or groups with varying intentions (The Alan Turing Institute).

Understanding the risks associated with large language models like GPT-3 is essential for developing strategies to counteract their potential misuse. The open-source nature of some models, while fostering innovation, also introduces vulnerabilities that must be addressed through robust governance frameworks. OpenAI’s mission to ensure AGI benefits humanity underscores the need for proactive measures to prevent its exploitation for malicious purposes (OpenAI). This includes investing in detection technologies, promoting digital literacy, and establishing ethical guidelines for the development and deployment of such systems. The broader implications of GPT-3’s capabilities highlight the importance of balancing technological advancement with societal responsibility. As these models continue to evolve, their impact on information ecosystems will depend on the collective efforts of researchers, policymakers, and the public to safeguard against their misuse, ensuring the potential of these technologies to serve the greater good (The Alan Turing Institute).

How LLMs Industrialize Disinformation

The rise of large language models has transformed the digital landscape, offering unprecedented capabilities while simultaneously creating new vulnerabilities that hostile actors exploit. In an era where information spreads faster than ever, the intersection of artificial intelligence and disinformation has become a critical concern. The ability of large language models to generate text indistinguishable from human output has made them powerful tools for shaping narratives, manipulating public perception, and advancing strategic interests. This dynamic underscores the growing relevance of understanding how these technologies are weaponized, particularly as they enable adversaries to bypass traditional barriers to influence. The implications extend beyond mere misinformation; they touch on the integrity of knowledge systems, the erosion of trust in institutions, and the potential for AI to become a central instrument in geopolitical and ideological battles. As the world grapples with the dual-edged nature of technological advancement, the role of these models in amplifying disinformation campaigns demands urgent scrutiny (The Alan Turing Institute).

Large language models, or LLMs, are sophisticated systems trained on vast datasets to generate coherent and contextually relevant text. These models, such as those powering ChatGPT, Claude, and Bard, can produce everything from news articles to code and creative writing, often with remarkable fluency. Their development is driven by the pursuit of artificial general intelligence, a goal that has captured the attention of researchers and investors alike. However, their capabilities are not without limitations. While LLMs excel at generating text, they lack true understanding, often producing outputs that are statistically plausible but factually incorrect or contextually inappropriate. Additionally, their training data is finite, meaning they cannot access real-time information or adapt to evolving circumstances. These constraints shape both the strengths of LLMs and the inherent risks they pose when misused (The Alan Turing Institute).

Disinformation campaigns have long been a tool for advancing political, economic, and social agendas, but the advent of LLMs has expanded their scope and scale. Hostile actors now have access to tools that can automate the creation of misleading content at an unprecedented speed and volume. This shift has made disinformation more pervasive, as adversaries can generate tailored messages that resonate with specific audiences while evading detection. For example, in the context of modern warfare, disinformation is no longer limited to human-operated efforts; it now includes strategies that target the very datasets used to train AI systems. This approach allows hostile actors to shape the information environment in ways that influence public understanding and decision-making (Insight News Media). By infiltrating the data pipelines that feed AI models, adversaries can alter the narratives these systems generate, effectively controlling the flow of information at a systemic level (The Alan Turing Institute).

The integration of LLMs into disinformation strategies has also enabled the creation of highly convincing but false narratives that blur the lines between truth and fabrication. Unlike traditional disinformation, which relies on human creators to produce content, LLMs can generate vast quantities of text with minimal oversight, making it easier to overwhelm digital platforms and erode public trust. This capability is particularly concerning because it allows hostile actors to amplify their influence without direct attribution, compounding the challenge of accountability. Moreover, the sheer scale of LLM-generated content makes it difficult for fact-checkers and moderators to keep pace, creating a feedback loop where misinformation spreads rapidly before it can be addressed. In this way, hostile actors turn the very technology designed to enhance communication into a weapon against it (The Alan Turing Institute).

The implications of this shift extend beyond individual platforms or regions, as the tools and tactics used in disinformation campaigns are increasingly globalized. Hostile actors can exploit the decentralized nature of the internet and the cross-border operation of AI systems to launch campaigns that target multiple jurisdictions simultaneously. This complexity is further compounded by the fact that LLMs can be trained on datasets that include information from diverse sources, making it easier to craft content that appears credible to a wide audience. As a result, disinformation campaigns are becoming more sophisticated, leveraging the technical capabilities of LLMs to achieve strategic objectives that range from destabilizing governments to manipulating public opinion. The challenge now lies in developing countermeasures that can mitigate these risks without stifling the benefits of AI innovation. This requires a coordinated effort among governments, technology companies, and civil society to safeguard the integrity of information systems (The Alan Turing Institute).

The Erosion of Shared Factual Reality

The allure of large language models (LLMs) for hostile actors lies in their ability to generate vast quantities of text with minimal human intervention, enabling the rapid dissemination of disinformation at scale. These models, which underpin systems like ChatGPT, Claude, and Bard, are designed to mimic human language patterns, making their outputs indistinguishable from authentic content to untrained observers. This technical capability allows malicious actors to bypass traditional gatekeeping mechanisms, flooding online platforms with fabricated narratives that can shape public perception without immediate detection. The ease with which LLMs can be repurposed for such ends has made them a cornerstone of modern disinformation strategies, as emphasized by Dame Emily Thornberry, who described disinformation as the “weapon of choice” for hostile states. The models’ adaptability further amplifies their threat, as they can be fine-tuned to exploit cultural or political contexts, tailoring disinformation to specific audiences with remarkable precision (The Mirror).

Historical and contemporary examples underscore the tangible impact of LLMs in disinformation campaigns. In 2020, during the U.S. Presidential election, malicious actors leveraged AI-generated text to spread false claims about voter fraud, exploiting the speed and scale of LLMs to overwhelm fact-checking efforts. More recently, Russia’s cognitive warfare strategies, as detailed in a 2026 analysis, reveal how hostile actors have sought to seed manipulated material into public archives, ensuring that both current social media feeds and future automated historical summaries reflect their preferred narratives. This approach transforms disinformation into a long-term infrastructure of influence, blurring the line between fact and fiction over time. The integration of LLMs into such strategies allows adversaries to maintain a persistent presence in public discourse, even as factual realities shift (Insight News Media).

Mitigating the risks posed by LLMs requires a multifaceted approach that combines technological innovation with institutional vigilance. One critical strategy involves developing advanced detection tools capable of identifying AI-generated content, such as anomalies in language patterns or inconsistencies in metadata. However, the sophistication of LLMs means these tools must continuously evolve to keep pace with adversarial techniques. Another key measure is the regulation of public archives, ensuring that historical records remain transparent and verifiable to prevent the entrenchment of manipulated narratives. This could involve collaboration between governments, tech companies, and academic institutions to establish standardized protocols for content curation and accountability. Additionally, fostering digital literacy among the public is essential, equipping citizens to question sources and recognize the potential for AI-driven deception (The Alan Turing Institute).

Looking ahead, the proliferation of LLMs raises profound implications for national security, public trust, and individual privacy. In the realm of national security, the ability of hostile actors to manipulate historical records through LLMs could erode the integrity of democratic institutions, as fabricated narratives become indistinguishable from verified facts. Public trust in media and government institutions may further deteriorate as citizens struggle to discern truth from falsehood, leading to societal polarization and decreased civic engagement. Meanwhile, the data-intensive nature of LLM training poses privacy risks, as the vast datasets used to train these models often include sensitive personal information. If exploited, this data could be weaponized to target individuals or groups, exacerbating existing vulnerabilities. Addressing these challenges will require proactive governance, ethical frameworks for AI development, and a commitment to transparency in the deployment of LLMs. The stakes are high; without such safeguards, the tools that promise to expand access to information could also become instruments of pervasive influence and control (The Alan Turing Institute).

Conclusion

The dual-use nature of large language models (LLMs) has created a paradoxical situation where their immense utility is simultaneously a source of vulnerability. As these models become more sophisticated, their capacity to generate coherent, contextually relevant text has made them indispensable tools for both constructive and destructive purposes. However, this same capability has also made them attractive to hostile actors seeking to exploit their power for disinformation campaigns.

The ability of LLMs to rapidly produce content that mimics human writing has enabled the proliferation of falsehoods on an unprecedented scale, undermining trust in information ecosystems and distorting public discourse. The inherent design of these models, which relies on vast amounts of training data to generate responses, introduces a critical weakness: the data itself can contain biases, inaccuracies, or malicious inputs that are amplified during the model’s output.

This raises urgent questions about the ethical and technical frameworks needed to govern their deployment. Addressing these vulnerabilities requires a multifaceted approach that balances innovation with accountability, ensuring that the benefits of LLMs are not overshadowed by their potential for harm (The Alan Turing Institute).

The challenge of mitigating the risks posed by LLMs lies in reconciling their power with the need for transparency and oversight. Current efforts to enhance model robustness, such as improving detection mechanisms for deceptive content or refining training data curation, are essential but insufficient. The YouTube video highlights how even well-intentioned models can be manipulated to produce harmful outputs, underscoring the necessity of proactive safeguards.

For instance, techniques like adversarial training, where models are exposed to counterexamples to reduce susceptibility to manipulation, have shown promise but remain underdeveloped. Additionally, the lack of standardized protocols for auditing and certifying LLMs exacerbates the problem, as stakeholders often operate in silos without shared accountability. To counteract these risks, collaboration across sectors, governments, tech companies, and civil society, is imperative.

This includes establishing transparent mechanisms for reporting and addressing misuse, as well as investing in research to better understand the long-term societal impacts of disinformation generated by these models. Without such measures, the potential for exploitation will only grow, as hostile actors refine their strategies to exploit gaps in model behavior and oversight (The Alan Turing Institute).

Looking ahead, the implications of this evolving landscape demand urgent attention from policymakers, technologists, and the public. The proliferation of disinformation through LLMs is not merely a technical issue but a societal one, requiring a reevaluation of how information is produced, validated, and consumed. One key open question is how to balance the need for open access to AI technologies with the imperative to prevent their misuse.

While innovation must not be stifled, the absence of regulatory frameworks could lead to a future where disinformation is both more pervasive and harder to trace. Readers should recognize that the fight against disinformation is not a static endeavor but an ongoing process that requires vigilance, education, and adaptive strategies. As LLMs become increasingly integrated into critical systems, from media to governance, the stakes of their misuse will only rise.

Ultimately, the path forward hinges on fostering a culture of responsibility that prioritizes the ethical deployment of these technologies while empowering individuals to critically engage with information. Detection tools such as Veritas and practical guidance on combating disinformation can support that effort. Only then can society harness the transformative potential of LLMs for the greater good (The Alan Turing Institute).

Sources

  1. insightnews. Available at: https://insightnews.media/russia-cognitive-warfare-in-2026-how-disinformation-became-an-architecture-of-influence/ [Accessed: 14 July 2026].
  2. indiatoday. Available at: https://www.indiatoday.in/technology/talking-points/story/with-ai-the-age-of-industrialised-falsehood-is-coming-to-india-2828229-2025-11-30 [Accessed: 14 July 2026].
  3. ibm. Available at: https://www.ibm.com/think/topics/large-language-models [Accessed: 14 July 2026].
  4. weforum. Available at: https://www.weforum.org/stories/digital-trust-and-safety/how-cognitive-manipulation-and-ai-will-shape-disinformation-in-2026/ [Accessed: 14 July 2026].
  5. mirror. Available at: https://www.mirror.co.uk/news/politics/emily-thornberry-disinformation-weapon-choice-36929426 [Accessed: 14 July 2026].
  6. stanford. Available at: https://fsi.stanford.edu/news/forecasting-potential-misuses-language-models-disinformation-campaigns-and-how-reduce-risk [Accessed: 14 July 2026].
  7. turing.ac.uk. Available at: https://www.turing.ac.uk/blog/llms-are-ever-more-convincing-important-consequences-election-disinformation [Accessed: 14 July 2026].
  8. en.wikipedia.org. Available at: https://en.wikipedia.org/wiki/Generative_pre-trained_transformer [Accessed: 14 July 2026].
  9. medium.com. Available at: https://medium.com/walmartglobaltech/the-journey-of-open-ai-gpt-models-32d95b7b7fb2 [Accessed: 14 July 2026].
  10. openai.com. Available at: https://openai.com/ [Accessed: 14 July 2026].
  11. mwi.westpoint.edu. Available at: https://mwi.westpoint.edu/disinformation-in-the-age-of-chatgpt/ [Accessed: 14 July 2026].
  12. sciencedirect.com. Available at: https://www.sciencedirect.com/science/article/pii/S2666827024000215 [Accessed: 14 July 2026].
  13. mwi.westpoint.edu. Available at: https://mwi.westpoint.edu/persuade-change-and-influence-with-ai-leveraging-artificial-intelligence-in-the-information-environment/ [Accessed: 14 July 2026].
  14. technologyreview.com. Available at: https://www.technologyreview.com/2023/10/04/1080801/generative-ai-boosting-disinformation-and-propaganda-freedom-house/ [Accessed: 14 July 2026].
  15. arxiv.org. Available at: https://arxiv.org/abs/2401.01519 [Accessed: 14 July 2026].
  16. arxiv.org. Available at: https://arxiv.org/html/2406.12935v1 [Accessed: 14 July 2026].
  17. research.tudelft.nl. Available at: https://research.tudelft.nl/en/publications/a-security-risk-taxonomy-for-prompt-based-interaction-with-large-/ [Accessed: 14 July 2026].
  18. youtube.com. Available at: https://www.youtube.com/watch?v=zjkBMFhNj_g [Accessed: 14 July 2026].
  19. journals.plos.org. Available at: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0317421 [Accessed: 14 July 2026].
  20. frontiersin.org. Available at: https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1543603/full [Accessed: 14 July 2026].
  21. nature.com. Available at: https://www.nature.com/articles/s43588-025-00890-x [Accessed: 14 July 2026].