Feb 02, 2026
3 min read
Language patterns are already present in everyday devices, but their use is a security, risk and data protection issue that needs to be understood.
Hidden Dangers of Everyday AI: Are Language Models Safe?
Based on facts directly observed and confirmed by our journalists or sources.
The mass adoption of AI in business and private use is advancing faster than regulatory approaches, opening up new challenges in data protection, privacy and cyber security.
The advent of large-scale language models (LLM) has changed the way people work, communicate, and innovate.This advanced form of artificial intelligence (AI), whether it's called ChatGPT, Gemini, or any other name, has huge transformative power, enabling faster workflows, deeper insights, and smarter tools.
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Trained to work with massive amounts of data, LLMs not only impress with human-like texts, but can be used in many contexts.Although this increasing adoption also comes with responsibility.If something goes wrong, the consequences can be serious, from the exposure of sensitive data, the spread of harmful or misleading content, the violation of regulations, and even the loss of trust in AI systems.
LLMs are so persuasive that it can sometimes be forgotten that they can be wrong. Furthermore, the more you depend on them, the harder it will be to question what they say. Therefore, in parallel with building more intelligent models, it is important to maintain a critical vision of what the model may or may not ignore.
Traditional cybersecurity, on the other hand, was not designed with graduate studies in mind, and this opened up a new category of vulnerabilities to combat.Far from predictable results, masters produce a dynamic and risky language that cannot be corrected or audited in the same way as other systems.Because wizards operate as "black boxes," it's difficult to understand how they produce certain results, making it difficult to spot instructions or potential problems like injection and data poisoning.
In addition to being clever with commands to work with LLM, cybercriminals can guess what data model has been generated or cause a connection to insecure APIs or third-party plugins.Another malicious tactic would be to overload models with long and repetitive instructions, which can slow down or crash AI services.However, large-scale social engineering phishing is currently the most common method used by attackers, as LLM facilitates the creation and distribution of credible messages that mimic legitimate communications for identity theft and data breaches.
When it comes to fast and powerful evolving technology, the challenge is more unique and security measures must be robust to ensure data protection and current regulations.As LLMs integrate into everyday tools for users like Google Workspace and Microsoft 365, the AI trend doesn't seem to be slowing down, so defenses need to keep up with the pace to uncover any security blind spots.
The risks associated with LLM are not a concern in the future.A few years ago, Samsung engineers pushed the company's source code and internal information to ChatGPT to help decipher the code and summarize the logs.There was no malice behind it, it was just part of normal business.However, because ChatGPT stores user input to improve its performance, it was concerned about the disclosure of trade secrets, so Samsung banned ChatGPT and its internal AI tool after the incident.created by
That's the case with DeepSeek AI, a Chinese startup with a more powerful and affordable model than others, but it stores user data on servers accessed by the Chinese government, raising concerns about the privacy and security of that data.
When it comes to security with LLM, the first thing to do is to limit the data you share to what is absolutely necessary and always review your responses to avoid revealing confidential information.From a technical perspective, it is advisable to implement role-based access controls, apply security restrictions, and conduct regular audits and penetration tests that specifically address the risks associated with LLM.
Traditional data security strategies must evolve to include adaptive capabilities and intelligent response systems that are suitable for an AI environment, verifying users, preventing unauthorized access, and constantly evaluating every interaction.By following this process, LLMs will gain confidence and the path to new ideas and innovation can be secured.
