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RatGPT: Turning online LLMs into Proxies for Malware Attacks
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The evolution of Generative AI and the capabilities of the newly released Large Language Models (LLMs) open new opportunities in software engineering. However, they also lead to new challenges in cybersecurity. Recently, researchers have shown the possibilities of using LLMs such as ChatGPT to generate malicious content that can directly be exploited or guide inexperienced hackers to weaponize tools and code. These studies covered scenarios that still require the attacker to be in the middle of the loop. In this study, we leverage openly available plugins and use an LLM as proxy between the attacker and the victim. We deliver a proof-of-concept where ChatGPT is used for the dissemination of malicious software while evading detection, alongside establishing the communication to a command and control (C2) server to receive commands to interact with a victim's system. Finally, we present the general approach as well as essential elements in order to stay undetected and make the attack a success. This proof-of-concept highlights significant cybersecurity issues with openly available plugins and LLMs, which require the development of security guidelines, controls, and mitigation strategies.
Forward citations
Cited by 3 Pith papers
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Ransomware 3.0: Self-Composing and LLM-Orchestrated
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LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems
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MGC: A Compiler Framework Exploiting Compositional Blindness in Aligned LLMs for Malware Generation
MGC, a two-stage compiler framework, generates functional malware by decomposing malicious intents into benign-appearing MDIR components that strong aligned LLMs will implement, bypassing safety alignment.
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