Metamorphic Malware Evolution: The Potential and Peril of Large Language Models

Code metamorphism refers to a computer programming exercise wherein the program modifies its own code (partial or entire) consistently and automatically while retaining its core functionality. This technique is often used for online performance optimization and automated crash recovery in certain mission-critical applications. However, the technique has been misappropriated by malware creators to bypass signature-based detection measures instituted by anti-malware engines. However, current code mutation engines used by threat actors offer only a limited degree of mutation, which is frequently detectable via static code analysis. The advent of large language models (LLMs), such as ChatGPT 4.0 and Google Bard may lead to a significant evolution in this landscape. These models have demonstrated a level of algorithm comprehension and code synthesis capability that closely resembles human abilities. This advancement has sparked concerns among experts that such models could be exploited by threat actors to generate sophisticated metamorphic malware. This paper explores the potential of several prominent LLMs for software code mutation that may be used to reconstruct (with mutation) existing malware code bases or create new forms of embedded mutation engines for next-gen metamorphic malwares. In this work, we introduce a framework for creating self-testing program mutation engines based on LLM/Transformer-based models. The proposed framework serves as an essential tool in testing next-gen metamorphic malware detection engines.

Noise as a Double-Edged Sword: Reinforcement Learning Exploits Randomized Defenses in Neural Networks

This study investigates a counterintuitive phenomenon in adversarial machine learning: the potential for noise-based defenses to inadvertently aid evasion attacks in certain scenarios. While randomness is often employed as a defensive strategy against adversarial examples, our research reveals that this approach can sometimes backfire, particularly when facing adaptive attackers using reinforcement learning (RL). Our findings show that in specific cases, especially with visually noisy classes, the introduction of noise in the classifier's confidence values can be exploited by the RL attacker, leading to a significant increase in evasion success rates. In some instances, the noise-based defense scenario outperformed other strategies by up to 20\% on a subset of classes. However, this effect was not consistent across all classifiers tested, highlighting the complexity of the interaction between noise-based defenses and different models. These results suggest that in some cases, noise-based defenses can inadvertently create an adversarial training loop beneficial to the RL attacker. Our study emphasizes the need for a more nuanced approach to defensive strategies in adversarial machine learning, particularly in safety-critical applications. It challenges the assumption that randomness universally enhances defense against evasion attacks and highlights the importance of considering adaptive, RL-based attackers when designing robust defense mechanisms.