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Found 14 papers, 4 papers with code
Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits
As a first step towards democratic governance and risk assessment of AI, we introduce Particip-AI, a framework to gather current and future AI use cases and their harms and benefits from non-expert public.
SecGPT: An Execution Isolation Architecture for LLM-Based Systems
These LLM apps leverage the de facto natural language-based automated execution paradigm of LLMs: that is, apps and their interactions are defined in natural language, provided access to user data, and allowed to freely interact with each other and the system.
LLM Platform Security: Applying a Systematic Evaluation Framework to OpenAI's ChatGPT Plugins
In this paper, we propose a framework that lays a foundation for LLM platform designers to analyze and improve the security, privacy, and safety of current and future plugin-integrated LLM platforms.
Is the U.S. Legal System Ready for AI's Challenges to Human Values?
Our interdisciplinary study investigates how effectively U. S. laws confront the challenges posed by Generative AI to human values.
Re-purposing Perceptual Hashing based Client Side Scanning for Physical Surveillance
Content scanning systems employ perceptual hashing algorithms to scan user content for illegal material, such as child pornography or terrorist recruitment flyers.
Reliable and Trustworthy Machine Learning for Health Using Dataset Shift Detection
We then translate the out-of-distribution score into a human interpretable CONFIDENCE SCORE to investigate its effect on the users' interaction with health ML applications.
Disrupting Model Training with Adversarial Shortcuts
When data is publicly released for human consumption, it is unclear how to prevent its unauthorized usage for machine learning purposes.
FoggySight: A Scheme for Facial Lookup Privacy
Searches in these databases are now being offered as a service to law enforcement and others and carry a multitude of privacy risks for social media users.
Security and Machine Learning in the Real World
Machine learning (ML) models deployed in many safety- and business-critical systems are vulnerable to exploitation through adversarial examples.
Physical Adversarial Examples for Object Detectors
In this work, we extend physical attacks to more challenging object detection models, a broader class of deep learning algorithms widely used to detect and label multiple objects within a scene.
Robust Physical-World Attacks on Deep Learning Visual Classification
Recent studies show that the state-of-the-art deep neural networks (DNNs) are vulnerable to adversarial examples, resulting from small-magnitude perturbations added to the input.
Note on Attacking Object Detectors with Adversarial Stickers
Given the fact that state-of-the-art objection detection algorithms are harder to be fooled by the same set of adversarial examples, here we show that these detectors can also be attacked by physical adversarial examples.
Robust Physical-World Attacks on Deep Learning Models
We propose a general attack algorithm, Robust Physical Perturbations (RP2), to generate robust visual adversarial perturbations under different physical conditions.
To Make a Robot Secure: An Experimental Analysis of Cyber Security Threats Against Teleoperated Surgical Robots
Our work seeks to answer this question by systematically analyzing possible cyber security attacks against Raven II, an advanced teleoperated robotic surgery system.
Robotics Cryptography and Security
