LLM’s in The Workplace – Is your Chatbot a Cyber Security Timebomb?
Large Language Models (LLMs) like ChatGPT, Claude, and Gemini have quickly become a key part of many corporate workflows. They’re used to automate customer service, generate code, draft internal documents, and much more. For many teams, these tools now serve as powerful digital co-pilots. But as adoption rises, so too does an important question: are they introducing new cyber security vulnerabilities?
The short answer is yes. If not carefully managed, LLMs and AI chatbots can expose your organisation to serious risks - ranging from data leaks to sophisticated phishing attacks. The same qualities that make these systems so flexible and useful; open-ended interaction, fast access to information, and wide-ranging integrations, can also create significant blind spots.
The Risk of Data Leakage
Perhaps the most immediate concern is the risk of sensitive data leakage. It’s increasingly common for employees to input confidential or proprietary information into AI tools while seeking help with emails, reports, coding problems, or summaries. This information could include personal data, customer records, financial details, source code, or internal strategy documents.
While most reputable AI providers state they do not use user input to train their models, some temporary logging and metadata collection is often necessary for service quality or debugging. In regulated sectors, this can raise serious compliance issues - especially under frameworks such as the UK GDPR, the Data Protection Act 2018, or industry-specific rules like FCA guidance.
In one notable incident, Samsung engineers accidentally submitted confidential semiconductor source code to ChatGPT while troubleshooting development tasks. Situations like this serve as a warning: AI tools are only as safe as the policies and awareness that govern their use.
Prompt Injection and Malicious Manipulation
A more technical but equally concerning threat is prompt injection. This occurs when a user manipulates the inputs sent to an AI model in order to override its intended instructions or security boundaries. If a chatbot has access to internal systems such as ticketing platforms, CRMs, or data repositories - prompt injection could be used to expose information or perform unauthorised actions.
For instance, an attacker might embed hidden commands in what appears to be a harmless message, instructing the chatbot to leak private data or act in unexpected ways. If the LLM has broad permissions and lacks proper constraints, the consequences can be serious.
This issue becomes more acute in setups that use retrieval-augmented generation (RAG) or connect LLMs to real-time systems. Without robust input validation and access controls, organisations may unknowingly be building insecure automation.
LLMs Fuel Advanced Social Engineering
One of the most practical risks today is the rise of AI-enhanced social engineering. Cyber criminals now use LLMs to generate highly convincing phishing emails, spoofed messages, and fake dialogues that are linguistically accurate, context-aware, and harder than ever to detect.
With just a small amount of personal information - often sourced from LinkedIn or previous data breaches - attackers can produce persuasive emails that impersonate executives, suppliers, or internal departments. This significantly raises the threat level of business email compromise (BEC) and impersonation attacks.
Traditional security awareness training often focuses on spotting typos, strange formatting, or awkward grammar. But when messages are written by an LLM, those old red flags may no longer apply.
Insecure Code Generation Is a Silent Threat
AI tools have rapidly found their way into software development workflows. Developers frequently rely on platforms like GitHub Copilot or ChatGPT to generate boilerplate code, solve logic problems, or write infrastructure scripts. While this can speed up delivery, it also introduces risk.
LLMs are not infallible. They may generate code that appears functional but lacks key security measures such as proper access controls or encryption.
Organisations must be especially cautious with AI-generated configurations and deployment scripts. These may include overly permissive IAM roles, unsecured S3 buckets, or default credentials - creating gaps that attackers can exploit.
The Risk Within Internal AI Integrations
As companies embed AI assistants into internal systems - such as HR bots, finance tools, or IT helpdesks, the risk shifts from external misuse to internal exploitation. Poorly designed integrations can act as a bridge between public-facing interfaces and sensitive back-end systems.
For example, a chatbot connected to a staff directory might unintentionally allow users to query information beyond their role. If the bot lacks proper role-basedaccess control (RBAC), input validation, or logging, it could be exploited by an insider or compromised account.
These integrations must be treated with the same rigour as any production system: code should be audited, traffic should be monitored, and escalation paths clearly defined.
Model Poisoning and Data Integrity Concerns
For organisations training their own models or fine-tuning open-source ones, another subtle risk emerges - model poisoning.
If training data includes manipulated content (either accidentally or deliberately) the resulting model can become biased, misleading, or even insecure.
Threat actors may try to introduce malicious patterns into publicly available training sets or supply poisoned data via seemingly innocent contributions. This can lead to harmful behaviours that are hard to detect until it's too late.
As a result, data curation and verification are just as critical as model evaluation when building internal LLM solutions.
What Can Organisations Do to Stay Secure?
The first line of defence is awareness. Organisations must recognise that AI is not magic - it is software with all the same risks, limitations, and attack surfaces as any other digital system. Clear internal policies are essential. Employees should be explicitly instructed not to input sensitive data into unauthorised AI tools. Approved platforms should be documented, and acceptable use guidelines communicated during training and onboarding.
Technically, organisations should favour enterprise-grade AI providers that offer robust privacy controls, access logging, and regional data hosting. For highly sensitive use cases, deploying LLMs in a private cloud or on-premises environment may be the safest option.
Additionally, all AI-connected applications must include basic safeguards such as:
- Input sanitisation
- Output filtering
- Authentication
- Rate limiting
Logs of all AI interactions should be stored and monitored for anomalies, and AI-generated content, whether code, reports, or emails, should be reviewed before use in production or client-facing contexts.
Finally, staff training must evolve. It’s no longer enough to teach people to spot obvious phishing emails. Employees must be trained to understand the new risks posed by AI, such as convincing deepfakes, manipulated outputs, and the tendency for LLMs to "hallucinate" false information based on incorrect search engine crawling.
Trust, but Verify.
LLMs and AI chatbots are undoubtedly powerful tools that can unlock new levels of productivity and creativity on an efficiency level not seen before. However, they also come with real-world security risks - some visible, others less so.
As adoption continues to grow, so must our commitment to deploying these technologies safely. With careful governance and a well-informed workforce, organisations can confidently embrace AI without sacrificing their security posture.
Those who strike the right balance between innovation and caution will be the ones best prepared for the future of intelligent enterprise.
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