ChatGPT, a widely popular language model, has raised concerns in the cybersecurity community due to its potential for exploiting system vulnerabilities. Security experts have demonstrated that ChatGPT and other large language models (LLMs) can generate polymorphic code, which mutates to evade endpoint detection and response (EDR) systems. Proof-of-concept attacks have shown how benign-looking executable files can make API calls to ChatGPT, prompting the generation of dynamic and mutating versions of malicious code that are difficult to detect.
Prompt engineering, the practice of modifying input prompts to bypass content filters, plays a crucial role in these exploits. By framing prompts as hypotheticals or requests for code with specific functionality, users can trick ChatGPT into generating effective malicious code. These techniques allow the creation of polymorphic malware that evades threat scanners and exfiltrates data. Several proof-of-concept programs, such as BlackMamba and ChattyCaty, have demonstrated the capabilities of ChatGPT in developing advanced and polymorphic malware.
Regulating generative AI presents challenges, as the industry is still grappling with understanding the technology’s potential. Experts suggest incorporating better explainability, observability, and context into AI systems to add meaningful layers of control. However, the difficulty lies in determining how to regulate and hold accountable the use of generative AI, as it offers endless possibilities and covers diverse circumstances.
Source: www.csoonline.com
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