Companies are increasingly adopting LLM in their processes, but they reveal flaws in terms of cyberrencies. However, their development also makes cybersecurity more effective.
The large Language Models (LLM) upset the generative AI, but their massive integration into the information systems causes new cybermenaces. OWASP (Open Worldwide Application Security Project) alerts the critical vulnerabilities of these models, often underestimated. Faced with the multiplication of threats, ranging from prompt injection to poisoning data, securing LLM becomes a priority issue. It therefore becomes essential to understand the risks and respond to it, to anticipate the uses to come and master its drifts.
LLM: a technological feat that sees flaws emerge
For many companies, LLMs are today an essential element of modern digital architectures. Their ability to understand and generate natural language opens up several perspectives such as virtual assistants or the automation of tasks. But this power is double-tranchant. By accepting open, sometimes blurred instructions, LLM can become the target of more methodical attacks. For example, in the case of a prompt injection, a malicious user can manipulate the initial request to divert the model’s response, obtain sensitive information or bypass safety rules.
To this are added other less visible, but just as strategic risks: the extraction of information memorized in the model, especially if it was exposed to sensitive data during its training, or even the adversarial attacks which exploit linguistic inaccuracies to cause erroneous responses. These flaws, inherent in the operation of the LLM, require a paradigm shift in the way of approaching their safety.
Threat cartography
Known for its web vulnerability rankings, OWASP recently shared its TOP 10 LLM threats. Beyond technical terminology stands a reality: LLM can no longer be perceived as simple tools. If they are not filtered or controlled, their responses can become vectors of malicious actions.
Among the threats listed, some are likely to be mistaken for classic web vulnerabilities, transposed to the context of generative AI: unsecured treatment of outputs, integration of plugins not verified or attacks by denial of service aimed at exhausting the resources of the model. Others, on the other hand, are specific such as model flight or poisoning of training data. The latter consists in introducing biases or errors in the data corpus used to shape the model, thus compromising its long -term reliability.
This reading grid does not only aim to classify threats, but to encourage a change in practices: to consider LLM as critical components of the information system, in the same way as a server, a database or an exposed API.
LLM protection: between strategic emergency and technical challenge
Faced with this multitude of threats constantly evolving, securing LLM cannot be deferred. It involves a global approach, combining governance, engineering and active surveillance. It is advisable to supervise uses: no model should be deployed without a rigorous phase of evaluation of its entries and exits, or without a real -time control mechanism of its behavior.
It also becomes essential to integrate devices for detecting anomalies, capable of identifying an attempt at injection or a sequence of suspicious requests. Likewise, data pipelines should be protected upstream, to prevent the model from implicitly learning confidential or handled data. Finally, like any software component, an LLM requires regular updates, not only to improve performance, but also to correct a posteriori vulnerabilities.
Ultimately, the rise in LLM must be accompanied by a riding in maturity of security practices. The actors who will be able to anticipate these issues will have a strategic advantage. For others, the discovery of vulnerabilities will often be in an emergency, or even after an attack.
