Early AI research exemplified by Prolog and chatbots like Eliza laid the groundwork for today’s advanced LLMs. These early systems, while limited by data availability and computational power, demonstrated the potential for human-computer interaction.
Over time advancements in NLP, machine learning, and deep learning enabled the development of sophisticated LLMs like ChatGPT. While LLMs offer numerous benefits, their powerful capabilities, such as generating human-like text, translating languages, and writing different kinds of creative content, can also be exploited by cybercriminals.
It is possible to use these tools to generate malicious code, craft phishing emails that are extremely convincing, and automate social engineering attacks that pose a significant threat to cybersecurity.
Large Language Models (LLMs) have significantly advanced Natural Language Processing (NLP) capabilities, enabling them to automate various stages of the cyberattack lifecycle.
By leveraging LLMs, attackers can efficiently gather information about targets through automated reconnaissance that includes identifying vulnerabilities and potential entry points.
It is demonstrated by their ability to interact with platforms like Censys and leverage tools like PentestGPT and Mantis to streamline the information gathering process and enhance the speed and efficiency of initial reconnaissance, posing a significant threat to organizations.
Big language models (LLMs) that are powered by artificial intelligence can be used to generate malicious code, send phishing emails, and obfuscate malware that already exists. I
Through the use of prompts, the LLM is deceived into providing instructions, and the communication is carried out by means of a proxy worker.
With the help of the LLM, the agent is able to bypass the conventional signature-based detection method by carrying out the instructions on the target machine and then transmitting the results back to the LLM.
LLMs now enable faster and more frequent attacks and to enhance the proof-of-concept, consider rewriting the code in Rust or Go for efficiency and less commonality.
Explore using local Tiny LLMs to eliminate reliance on external LLM servers. During initial LLM communication, instruct the agent to request and compile malware using a polymorphic algorithm.
Hunting for LLM-based malware is challenging, while research and potentially integrating this concept into Behavior Large Models (BLMs), though caution is strongly advised.
LLMs are not fundamentally altering the cyber threat landscape but rather amplifying existing attacks. By enhancing speed, accuracy, and scale across the entire attack lifecycle, LLMs empower threat actors, even those with limited expertise.
According to Deep Instinct, the evolution demands a proactive defense strategy that disrupts the attack chain at two critical stages. Achieving this necessitates equipping defenders with advanced tools capable of identifying and mitigating threats driven by LLM-powered techniques.
