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Penetration testing, an essential component of software security testing,allows organizations to identify and remediate vulnerabilities in theirsystems, thus bolstering their defense mechanisms against cyberattacks. Onerecent advancement in the realm of penetration testing is the utilization ofLanguage Models (LLMs). We explore the intersection of LLMs and penetrationtesting to gain insight into their capabilities and challenges in the contextof privilege escalation. We introduce a fully automated privilege-escalationtool designed for evaluating the efficacy of LLMs for (ethical) hacking,executing benchmarks using multiple LLMs, and investigating their respectiveresults.  Our results show that GPT-4-turbo is well suited to exploit vulnerabilities(33-83% of vulnerabilities). GPT-3.5-turbo can abuse 16-50% of vulnerabilities,while local models, such as Llama3, can only exploit between 0 and 33% of thevulnerabilities.  We analyze the impact of different context sizes, in-context learning,optional high-level guidance mechanisms, and memory management techniques. Wediscuss challenging areas for LLMs, including maintaining focus during testing,coping with errors, and finally comparing LLMs with human hackers.  The current version of the LLM-guided privilege-escalation prototype can befound at https://github.com/ipa-labs/hackingBuddyGPT.

LLMs as Hackers: Autonomous Linux Privilege Escalation Attacks