Peer-reviewed and working papers on AI safety, alignment, evaluation, robustness, and policy — all open-access under CC BY 4.0.
Peer-reviewed and working papers on AI safety, alignment, and risk.
We propose a quantitative framework for measuring the degree to which LLM outputs remain consistent with stated human values across diverse adversarial prompting conditions.
A systematic review of existing red-teaming methodologies across major AI labs, identifying critical gaps in coverage and proposing a standardized evaluation protocol.
Drawing on analogies from aviation and pharmaceutical regulation, we propose a tiered pre-deployment safety certification regime for AI systems above defined capability thresholds.
We identify and categorize 47 distinct patterns of specification gaming observed in RLHF-trained models and evaluate the effectiveness of proposed mitigation strategies.
An analysis of self-reported AI incidents from 2020–2025, demonstrating systemic underreporting and proposing a mandatory structured disclosure regime analogous to aviation near-miss reporting.
We examine the conditions under which corrigibility properties degrade as AI systems encounter out-of-distribution inputs and propose architectural interventions to preserve oversight mechanisms.
We present a comprehensive taxonomy of prompt injection attack vectors — direct, indirect, multi-turn, and cross-modal — and evaluate mitigation strategies across deployed LLM-integrated applications.
A systematic analysis of information poisoning threats across the full AI deployment lifecycle, introducing the IPTD framework and evaluating defenses for training, RAG, and agentic pipelines.