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PentestGPT: An LLM-empowered Automatic Penetration Testing Tool
Penetration testing, a crucial industrial practice for ensuring system security, has traditionally resisted automation due to the extensive expertise required by human professionals.
Software Engineering Cryptography and Security
Getting pwn'd by AI: Penetration Testing with Large Language Models
The field of software security testing, more specifically penetration testing, is an activity that requires high levels of expertise and involves many manual testing and analysis steps.
Modeling Penetration Testing with Reinforcement Learning Using Capture-the-Flag Challenges: Trade-offs between Model-free Learning and A Priori Knowledge
In this paper, we focus our attention on simplified penetration testing problems expressed in the form of capture the flag hacking challenges, and we analyze how model-free reinforcement learning algorithms may help to solve them.
Agent-based (BDI) modeling for automation of penetration testing
Penetration testing (or pentesting) is one of the widely used and important methodologies to assess the security of computer systems and networks.
Cryptography and Security Multiagent Systems
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.
SD-WAN Threat Landscape
We describe basic SD-WAN features and components, investigate an attack surface, explore various vendor features and their security, explain threats and vulnerabilities found in SD-WAN products.
Cryptography and Security
Resolving 3D Human Pose Ambiguities with 3D Scene Constraints
To motivate this, we show that current 3D human pose estimation methods produce results that are not consistent with the 3D scene.
Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primary
Cancer of unknown primary (CUP) is an enigmatic group of diagnoses where the primary anatomical site of tumor origin cannot be determined.
secml-malware: Pentesting Windows Malware Classifiers with Adversarial EXEmples in Python
Recent work has however shown that learning-based malware detectors can be evaded by carefully-perturbed input malware samples, referred to as adversarial EXEmples, thus demanding for tools that can ease and automate the adversarial robustness evaluation of such detectors.
Cryptography and Security
Understanding Hackers' Work: An Empirical Study of Offensive Security Practitioners
This analysis allows us to conclude with recommendations for researchers and tool builders to increase the efficiency of their automation and identify novel areas for research.
Software Engineering Cryptography and Security
