BitBypass: a New Direction in Jailbreaking Aligned Large Language Models with Bitstream Camouflage

The inherent risk of generating harmful and unsafe content by Large Language Models LLMs, has highlighted the need for their safety alignment. Various techniques like supervised fine-tuning, reinforcement learning from human feedback, and red-teaming were developed for ensuring the safety alignme...

CAIN: Hijacking LLM-Humans Conversations Via a Two-Stage Malicious System Prompt Generation and Refining Framework

Large language models LLMs have advanced many applications, but are also known to be vulnerable to adversarial attacks. In this work, we introduce a novel security threat: hijacking AI-human conversations by manipulating LLMs' system prompts to produce malicious answers only to specific targeted...

Input-Specific and Universal Adversarial Attack Generation for Spiking Neural Networks in the Spiking Domain

As Spiking Neural Networks SNNs gain traction across various applications, understanding their security vulnerabilities becomes increasingly important. In this work, we focus on the adversarial attacks, which is perhaps the most concerning threat. An adversarial attack aims at finding a subtle...

DYNAMITE: Dynamic Defense Selection for Enhancing Machine Learning-Based Intrusion Detection against Adversarial Attacks

The rapid proliferation of the Internet of Things IoT has introduced substantial security vulnerabilities, highlighting the need for robust Intrusion Detection Systems IDS. Machine learning-based intrusion detection systems ML-IDS have significantly improved threat detection capabilities; however...

Over 100 Malicious AI/ML Models Found on Hugging Face Platform

As many as 100 malicious artificial intelligence AI/machine learning ML models have been discovered in the Hugging Face platform. These include instances where loading a pickle file leads to code execution, software supply chain security firm JFrog said. "The model's payload grants the attacker a...

Manipulating Machine-Learning Systems through the Order of the Training Data

Yet another adversarial ML attack: Most deep neural networks are trained by stochastic gradient descent. Now “stochastic” is a fancy Greek word for “random”; it means that the training data are fed into the model in random order. So what happens if the bad guys can cause the order to be not rando...

‘Optical Adversarial Attack’ uses low-cost projector to trick AI

By Sudais Asif In the latest, we have another piece of research that deals with strikingly similar details but incorporating the trickery of Artificial Intelligence AI. This is a post from HackRead.com Read the original post: Optical Adversarial Attack uses low-cost projector to trick AI...

OpenAttack - An Open-Source Package For Textual Adversarial Attack

OpenAttack is an open-source Python-based textual adversarial attack toolkit, which handles the whole process of textual adversarial attacking, including preprocessing text, accessing the victim model, generating adversarial examples and evaluation. Features & Uses OpenAttack has following...

Machine learning classifiers trained via gradient descent are vulnerable to arbitrary misclassification attack

Overview Machine learning models trained using gradient descent can be forced to make arbitrary misclassifications by an attacker that can influence the items to be classified. The impact of a misclassification varies widely depending on the ML model's purpose and of what systems it is a part...