As published in HackerNoon and featured as a “Top 20 Best Read Article” for AI.
Introduction
The rapid advancement of AI has offered powerful tools for malware detection, but it has also introduced new avenues for adversarial attacks. As an example, recently OpenAI reported threat actors abusing ChatGPT to execute reconnaissance, help fix code, write partial code, or look at vulnerabilities. These are, to me, examples of AI aiding “basic” steps, but would threat actors invest and use more advanced applications?
Universal Adversarial Perturbations (UAPs) have gained attention due to their potential to bypass machine learning models in various domains, including malware detection. UAPs can manipulate malware in ways that evade AI-based detection systems without altering the malware's core functionality. However, despite this capability, cybercriminals have not widely adopted AI-driven techniques like UAPs. This blog delves into the complexity and effort required to generate UAPs for malware and explains why it might not be worth the trouble for attackers.
Just to be clear on definitions:
Artificial Intelligence (AI) is a broad field that aims to create machines or software capable of performing tasks that typically require human intelligence, such as understanding language, recognizing images, problem-solving, and decision-making. AI encompasses various techniques and approaches, from rule-based systems to learning algorithms.
Machine Learning (ML) is a subset of AI that focuses on building systems that learn from data. Instead of being explicitly programmed for each task, ML models identify patterns in data to make predictions or decisions, improving over time with more experience.
UAPs: A Brief Overview
Universal Adversarial Perturbations (UAPs) are subtle modifications applied to input data (such as malware samples) to mislead AI models. What makes UAPs particularly interest ..
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