In today’s digital age, artificial intelligence (AI) has transformed numerous aspects of our daily lives, and cybersecurity is no exception.
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.
In this article, we will explore the risks associated with LLMs, how attacks are modeled using AI, and how Microsoft’s Copilot for Security tool offers an innovative solution to address these threats.
Risks of Large Language Models (LLM)
LLM adoption has grown exponentially since late 2022, rapidly integrating into business operations and customer offerings.
However, this speed has outpaced the establishment of adequate security protocols, leaving many applications vulnerable to high-risk issues.
To address these challenges, the OWASP Top 10 list for LLM applications was created for developers, data scientists and security experts.
Among the vulnerabilities highlighted are malicious parameter injection, disclosure of sensitive information, insecure output handling, training data poisoning, excessive agency, overdependency, and model stealing.
For example, parameter injection occurs when an attacker manipulates an LLM through engineered inputs, which can lead to data leakage (by knowing how to “cheat” the model) and other problems.
Insecure output handling refers to insufficient validation and sanitization of LLM-generated results, which can result in privilege escalation or remote code execution.
Training data poisoning involves manipulation of pre-training data to introduce vulnerabilities or biases.
Attack modeling using Artificial Intelligence
AI represents not only a solution in cybersecurity, but also a significant threat. Attacks using AI technologies are becoming increasingly sophisticated and targeted, adapting to victims in a precise manner.
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.
Another example is the neural network brute-force attack, where attackers train a neural network to guess passwords using keyboard patterns.
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.
Copilot for Security: An innovative solution
To address these challenges, Microsoft has developed “Copilot for Security”, 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.
Copilot for Security offers several key advantages:
- Advanced Threat Detection: Uses advanced algorithms to detect and analyze threats that may go undetected by traditional security measures.
- Operational efficiency: Automates threat analysis, allowing security teams to focus on strategic decisions.
- Integration with Microsoft products: Integrates almost natively with other Microsoft products, creating a complete cybersecurity ecosystem.
- Continuous learning: AI and machine learning components ensure the tool is constantly evolving.
- Reduced false positives: Advanced algorithms minimize false positives, improving accuracy in threat detection.
In summary, the adoption of LLM and AI in cybersecurity presents both opportunities and challenges. It is crucial to address the risks associated with LLM and be prepared to face sophisticated attacks using AI.
Tools such as Microsoft’s Copilot for Security offer innovative solutions to improve security posture and protect organizations in this ever-evolving digital environment.
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