{"id":50674,"date":"2024-11-25T13:21:33","date_gmt":"2024-11-25T13:21:33","guid":{"rendered":"https:\/\/www.bravent.net\/?p=50674"},"modified":"2024-11-25T13:32:43","modified_gmt":"2024-11-25T13:32:43","slug":"the-cybersecurity-revolution-risks-of-large-language-models-llms-and-the-copilot-for-security-solution","status":"publish","type":"post","link":"https:\/\/www.bravent.net\/en\/news\/the-cybersecurity-revolution-risks-of-large-language-models-llms-and-the-copilot-for-security-solution","title":{"rendered":"The Cybersecurity revolution: Risks of Large Language Models (LLMs) and the Copilot for Security solution"},"content":{"rendered":"

In today’s digital age, artificial intelligence (AI)<\/a> has transformed numerous aspects of our daily lives, and cybersecurity is no exception.<\/p>\n

However, with the mass adoption of large language models (LLMs), new challenges and risks arise that must be addressed to ensure the security of applications and systems.<\/p>\n

In this article, we will explore the risks associated with LLMs, how attacks are modeled using AI, and how Microsoft’s Copilot for Security<\/a> tool offers an innovative solution to address these threats.<\/p>\n<\/div>[vc_empty_space]

Risks of Large Language Models (LLM)<\/h3>\n

LLM adoption has grown exponentially since late 2022, rapidly integrating into business operations and customer offerings.<\/p>\n

However, this speed has outpaced the establishment of adequate security protocols, leaving many applications vulnerable to high-risk issues.<\/p>\n

To address these challenges, the OWASP Top 10 list for LLM applications was created for developers, data scientists and security experts.<\/p>\n

Among the vulnerabilities highlighted are malicious parameter injection, disclosure of sensitive information, insecure output handling, training data poisoning, excessive agency, overdependency, and model stealing.<\/p>\n

For example, parameter injection occurs when an attacker manipulates an LLM through engineered inputs, which can lead to data leakage (by knowing how to \u201ccheat\u201d the model) and other problems.<\/p>\n

Insecure output handling refers to insufficient validation and sanitization of LLM-generated results, which can result in privilege escalation or remote code execution.<\/p>\n

Training data poisoning involves manipulation of pre-training data to introduce vulnerabilities or biases.<\/p>\n<\/div>[vc_empty_space]

Attack modeling using Artificial Intelligence<\/h3>\n

AI represents not only a solution in cybersecurity<\/a>, but also a significant threat. Attacks using AI technologies are becoming increasingly sophisticated and targeted, adapting to victims in a precise manner.<\/p>\n

One example of this is phishing with Generative Adversarial Networks (GANs), where attackers create GAN models trained to generate realistic images of login websites, tricking users into providing their credentials.<\/p>\n

Another example is the neural network brute-force attack, where attackers train a neural network to guess passwords using keyboard patterns.<\/p>\n

In addition, data poisoning attacks on machine learning models involve modifying training data so that a model misclassifies specific objects. These attacks can have devastating consequences, compromising the security and effectiveness of systems.<\/p>\n<\/div>[vc_empty_space]

Copilot for Security: An innovative solution<\/h3>\n

To address these challenges, Microsoft has developed \u201cCopilot for Security\u201d<\/a>, a tool integrated into the Microsoft Defender XDR platform that uses AI to improve cybersecurity. Copilot for Security enables rapid incident response, advanced threat hunting and the collection of threat intelligence information. One of its main advantages is that it integrates natural language, allowing simple questions to be asked for tailored advice and information.<\/p>\n

Copilot for Security offers several key advantages:<\/p>\n