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Articles by Apostol Vassilev
Computer Science > Cryptography and Security
Cooperative perception enables autonomous vehicles (AVs) to improve situational awareness by aggregating detection outputs from multiple agents and sensing platforms, often via a shared fusion service in multi-vendor deployments. However, sharing such outputs at inference time exposes proprietary model behavior and sensitive environmental information, creating significant privacy and security concerns.
Computer Science > Cryptography and Security
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Robust AI Security and Alignment: A Sisyphean Endeavor?
This manuscript establishes information-theoretic limitations for robustness of AI security and alignment by extending Gödel's incompleteness theorem to AI. Knowing these limitations and preparing for the challenges they bring is critically important for the responsible adoption of the AI technology. Practical approaches to dealing with these challenges are provided as well. Broader implications for cognitive reasoning limitations of AI systems are also proven.
Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations
, Alina Oprea, Alie Fordyce, Hyrum Anderson, Xander Davies, This NIST Trustworthy and Responsible AI report provides a taxonomy of concepts and defines terminology in the field of adversarial machine learning (AML). The taxonomy is arranged in a conceptual hierarchy that includes key types of ML methods, life cycle stages of attack, and attacker goals, objectives, capabilities, and knowledge.
Secure Software Development Practices for Generative AI and Dual-Use Foundation Models: An SSDF Community Profile
, , , , , Karen Scarfone This document augments the secure software development practices and tasks defined in Secure Software Development Framework (SSDF) version 1.1 by adding practices, tasks, recommendations, considerations, notes, and informative references that are specific to AI model development throughout the software development life cycle.
Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations
, Alina Oprea, Alie Fordyce, Hyrum Andersen This NIST AI report develops a taxonomy of concepts and defines terminology in the field of adversarial machine learning (AML). The taxonomy is built on survey of the AML literature and is arranged in a conceptual hierarchy that includes key types of ML methods and lifecycle stage of attack, attacker goals and objectives, and attacker capabilities and knowledge of the learning process.
META LEARNING WITH LANGUAGE MODELS: CHALLENGES AND OPPORTUNITIES IN THE CLASSIFICATION OF IMBALANCED TEXT
Detecting out of policy speech (OOPS) content is important but difficult. While machine learning is a powerful tool to tackle this challenging task, it is hard to break the performance ceiling due to factors like quantity and quality limitations on training data and inconsistencies in OOPS definition and data labeling. To realize the full potential of available limited resources, we propose a meta learning technique (MLT) that combines individual models built with different text representations.
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