Search Results
LLM Post-Training: A Deep Dive into Reasoning Large Language Models
Large Language Models (LLMs) have transformed the natural language processing landscape and brought to life diverse applications.
LLMs as Hackers: Autonomous Linux Privilege Escalation Attacks
We explore the intersection of LLMs and penetration testing to gain insight into their capabilities and challenges in the context of privilege escalation.
CIPHER: Cybersecurity Intelligent Penetration-testing Helper for Ethical Researcher
Additionally, we introduced the Findings, Action, Reasoning, and Results (FARR) Flow augmentation, a novel method to augment penetration testing write-ups to establish a fully automated pentesting simulation benchmark tailored for large language models.
The Agent Web Model -- Modelling web hacking for reinforcement learning
Website hacking is a frequent attack type used by malicious actors to obtain confidential information, modify the integrity of web pages or make websites unavailable.
Cryptography and Security
Towards Automated Penetration Testing: Introducing LLM Benchmark, Analysis, and Improvements
We first evaluate the performance of LLMs, including GPT-4o and LLama 3. 1-405B, using the state-of-the-art PentestGPT tool.
Machine Learning Featurizations for AI Hacking of Political Systems
What would the inputs be to a machine whose output is the destabilization of a robust democracy, or whose emanations could disrupt the political power of nations?
PenTest++: Elevating Ethical Hacking with AI and Automation
Traditional ethical hacking relies on skilled professionals and time-intensive command management, which limits its scalability and efficiency.
AI-Enhanced Ethical Hacking: A Linux-Focused Experiment
This technical report investigates the integration of generative AI (GenAI), specifically ChatGPT, into the practice of ethical hacking through a comprehensive experimental study and conceptual analysis.
AI-Augmented Ethical Hacking: A Practical Examination of Manual Exploitation and Privilege Escalation in Linux Environments
This study explores the application of generative AI (GenAI) within manual exploitation and privilege escalation tasks in Linux-based penetration testing environments, two areas critical to comprehensive cybersecurity assessments.
