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Found 14 papers, 2 papers with code
PyRIT: A Framework for Security Risk Identification and Red Teaming in Generative AI System
To meet this need, we introduce the Python Risk Identification Toolkit (PyRIT), an open-source framework designed to enhance red teaming efforts in GenAI systems.
Phi-3 Safety Post-Training: Aligning Language Models with a "Break-Fix" Cycle
Recent innovations in language model training have demonstrated that it is possible to create highly performant models that are small enough to run on a smartphone.
The Human Factor in AI Red Teaming: Perspectives from Social and Collaborative Computing
Rapid progress in general-purpose AI has sparked significant interest in "red teaming," a practice of adversarial testing originating in military and cybersecurity applications.
Adversarial Machine Learning and Cybersecurity: Risks, Challenges, and Legal Implications
In July 2022, the Center for Security and Emerging Technology (CSET) at Georgetown University and the Program on Geopolitics, Technology, and Governance at the Stanford Cyber Policy Center convened a workshop of experts to examine the relationship between vulnerabilities in artificial intelligence systems and more traditional types of software vulnerabilities.
Adversarial for Good? How the Adversarial ML Community's Values Impede Socially Beneficial Uses of Attacks
In this paper, we review the broader impact statements that adversarial ML researchers wrote as part of their NeurIPS 2020 papers and assess the assumptions that authors have about the goals of their work.
Ethical Testing in the Real World: Evaluating Physical Testing of Adversarial Machine Learning
This paper critically assesses the adequacy and representativeness of physical domain testing for various adversarial machine learning (ML) attacks against computer vision systems involving human subjects.
Computers and Society
Legal Risks of Adversarial Machine Learning Research
Adversarial Machine Learning is booming with ML researchers increasingly targeting commercial ML systems such as those used in Facebook, Tesla, Microsoft, IBM, Google to demonstrate vulnerabilities.
Adversarial Machine Learning -- Industry Perspectives
Based on interviews with 28 organizations, we found that industry practitioners are not equipped with tactical and strategic tools to protect, detect and respond to attacks on their Machine Learning (ML) systems.
Politics of Adversarial Machine Learning
In this paper, we draw on insights from science and technology studies, anthropology, and human rights literature, to inform how defenses against adversarial attacks can be used to suppress dissent and limit attempts to investigate machine learning systems.
Failure Modes in Machine Learning Systems
In the last two years, more than 200 papers have been written on how machine learning (ML) systems can fail because of adversarial attacks on the algorithms and data; this number balloons if we were to incorporate papers covering non-adversarial failure modes.
Law and Adversarial Machine Learning
When machine learning systems fail because of adversarial manipulation, how should society expect the law to respond?
Hardening Quantum Machine Learning Against Adversaries
Finally, we provide a private form of $k$--means clustering that can be used to prevent an all powerful adversary from learning more than a small fraction of a bit from any user.
Practical Machine Learning for Cloud Intrusion Detection: Challenges and the Way Forward
Operationalizing machine learning based security detections is extremely challenging, especially in a continuously evolving cloud environment.
