offensive-ai-compilation

Compilación de IA Ofensiva Una lista curada de recursos útiles que cubren la IA Ofensiva. 📁 Contenido 📁 🚫 Abuso 🚫 🧠 Aprendizaje Automático Adversario 🧠 ⚡ Ataques ⚡ 🔒 Extracción 🔒 ⚠️ Limitaciones ⚠️ 🛡️ Acciones defensivas 🛡️ 🔗 Enlaces útiles 🔗 ⬅️ Inversión o inferencia ⬅️ 🛡️ Acciones defensivas 🛡️ 🔗...

SecML

SecML: Una librería para Machine Learning Seguro y Explicable SecML es una librería de código abierto en Python para la evaluación de seguridad de algoritmos de Machine Learning ML. Viene con un conjunto de potentes características: Amplia gama de algoritmos ML soportados. Todos los algoritmos de...

Breaking Windows Malware Detection: A Comprehensive Evaluation of Problem-Space Adversarial Robustness

Problem-space evasion attacks have exposed critical weaknesses in machine learning-based malware detectors; yet, their evaluation remains fragmented across models, datasets, and attack methodologies, often neglecting domain-specific requirements such as executability and functionality preservatio...

Empirical Analysis of Evasion and Poisoning against Malware Data Drift Detection

As concept drift due to malware evolution presents challenges for malware classification, machine learning-based data drift detection tools are developed to mitigate this problem. These data drift detector tools are designed for a different purpose and built with different techniques compared to...

Signal-Based Model Access Risk Analysis for AI System Operations Security

Artificial intelligence AI systems are now ubiquitous across domains such as security, finance, healthcare, consumer technology, and large-scale cloud services, where they process massive volumes of data and make consequential decisions daily. This widespread adoption has created a broad attack...

Breaking TinyML: Why Quantized Neural Networks Need Domain-Specific Security Analysis

Most TinyML hardware accelerators focus on supporting Quantized Neural Networks QNNs to meet stringent constraints on power consumption and size. Despite this, the security aspects of quantization within TinyML hardware remain largely unexplored. Although previous studies indicate that QNNs...

Targeted Adversarial Traffic Generation : Black-Box Approach to Evade Intrusion Detection Systems in IoT Networks

The integration of machine learning ML algorithms into Internet of Things IoT applications has introduced significant advantages alongside vulnerabilities to adversarial attacks, especially within IoT-based intrusion detection systems IDS. While theoretical adversarial attacks have been extensive...

SourceBroken: A Large-Scale Analysis on the (Un)Reliability of SourceRank in the PyPI Ecosystem

SourceRank is a scoring system made of 18 metrics that assess the popularity and quality of open-source packages. Despite being used in several recent studies, none has thoroughly analyzed its reliability against evasion attacks aimed at inflating the score of malicious packages, thereby...

DeepTrust: Multi-Step Classification through Dissimilar Adversarial Representations for Robust Android Malware Detection

Over the last decade, machine learning has been extensively applied to identify malicious Android applications. However, such approaches remain vulnerable against adversarial examples, i.e., examples that are subtly manipulated to fool a machine learning model into making incorrect predictions...

DATABench: Evaluating Dataset Auditing in Deep Learning from an Adversarial Perspective

The widespread application of Deep Learning across diverse domains hinges critically on the quality and composition of training datasets. However, the common lack of disclosure regarding their usage raises significant privacy and copyright concerns. Dataset auditing techniques, which aim to...

Assessing the Resilience of Automotive Intrusion Detection Systems to Adversarial Manipulation

The security of modern vehicles has become increasingly important, with the controller area network CAN bus serving as a critical communication backbone for various Electronic Control Units ECUs. The absence of robust security measures in CAN, coupled with the increasing connectivity of vehicles,...

Learning from the Good Ones: Risk Profiling-Based Defenses against Evasion Attacks on DNNs

Safety-critical applications such as healthcare and autonomous vehicles use deep neural networks DNN to make predictions and infer decisions. DNNs are susceptible to evasion attacks, where an adversary crafts a malicious data instance to trick the DNN into making wrong decisions at inference time...

Overlapping Data in Network Protocols: Bridging OS and NIDS Reassembly Gap

IPv4, IPv6, and TCP have a common mechanism allowing one to split an original data packet into several chunks. Such chunked packets may have overlapping data portions and, OS network stack implementations may reassemble these overlaps differently. A Network Intrusion Detection System NIDS that...

NIST Warns of Security and Privacy Risks from Rapid AI System Deployment

The U.S. National Institute of Standards and Technology NIST is calling attention to the privacy and security challenges that arise as a result of increased deployment of artificial intelligence AI systems in recent years. "These security and privacy challenges include the potential for adversari...

PT-2022-23866 · Chipolo · Chipolo One Bluetooth Tracker +1

Name of the Vulnerable Software and Affected Versions: Chipolo ONE Bluetooth tracker 2020 version 4.13.0 Chipolo iOS app version 4.13.0 Description: The issue concerns Incorrect Access Control, allowing access revocation evasion attacks. Once a malicious sharee obtains access credentials, Chipolo...

Introduction and Application of Model Hacking

ARCHIVED STORY Introduction and Application of Model Hacking By Steve Povolny · Febraury 19, 2020 Catherine Huang, Ph.D., and Shivangee Trivedi contributed to this blog. The term “Adversarial Machine Learning” AML is a mouthful! The term describes a research field regarding the study and design o...

Introduction and Application of Model Hacking

ARCHIVED STORY Introduction and Application of Model Hacking By Steve Povolny · Febraury 19, 2020 Catherine Huang, Ph.D., and Shivangee Trivedi contributed to this blog. The term “Adversarial Machine Learning” AML is a mouthful! The term describes a research field regarding the study and design o...