Recent research has highlighted the potential cybersecurity threats posed by Large Language Models (LLMs), particularly GPT-4. Studies have shown that these AI agents can autonomously perform complex hacking tasks, such as blind database schema extraction and SQL injections, without prior knowledge of vulnerabilities and without human feedback. This capability raises significant concerns about the deployment of LLMs in real-world applications. Critics, however, have pointed out flaws in the research, including a lack of data, transparency in methodology, and comparison with traditional penetration testing tools.
I wrote down some quick thoughts on that "LLM Agents can Autonomously Hack Websites" paper that has been going around. TLDR; no data, lack of transparency in methodology, no baseline testing against traditional penetrating testing tools https://t.co/is1bGAeuGY
I wrote down some thoughts on that "LLM Agents can Autonomously Hack Websites" paper thats been going around. TLDR; no data, lack of transparency, no knowledge of existing traditional tools. https://t.co/is1bGAeuGY
🤯LLM Agents can Autonomously Hack Websites While recent research has speculated on the impact of LLM agents on cybersecurity, little is known about their offensive capabilities. The study demonstrates that LLM agents, particularly #GPT-4, can autonomously hack websites,… https://t.co/hVlbthimrn
LLMs poised to cause major cybersecurity problems: “we show that GPT-4 is capable of autonomously finding vulnerabilities in websites in the wild. Our findings raise questions about the widespread deployment of LLMs” @CISAJen https://t.co/B717dG2O1V
In this work, we show that LLM agents can autonomously hack websites, performing tasks as complex as blind database schema extraction and SQL injections without human feedback. Importantly, the agent does not need to know the vulnerability beforehand. https://t.co/lzLdAbkiKj