Independent research institute dedicated to advancing AI security, ethics, and privacy through rigorous analysis, principled solutions, and evidence-based policy recommendations.
Read Our ResearchWe conduct independent research to identify, analyze, and address critical challenges in artificial intelligence systems, with a focus on security vulnerabilities, ethical implications, and privacy considerations.
We employ rigorous methodologies to investigate AI systems, publishing peer-reviewed research that advances understanding of AI capabilities, limitations, and risks.
Our team develops practical tools, frameworks, and methodologies that help organizations deploy AI systems more safely and responsibly.
We work with policymakers and industry leaders to craft evidence-based regulations and standards that protect society while enabling beneficial innovation.
Our research spans critical domains where AI systems intersect with security, ethics, and privacy concerns.
Investigating vulnerabilities in AI systems, including adversarial attacks, model manipulation, and deployment security challenges.
Examining bias, fairness, transparency, and accountability in AI decision-making systems across various domains.
Developing and evaluating privacy-preserving techniques for AI systems, including differential privacy and federated learning approaches.
Creating frameworks for responsible AI governance, regulatory compliance, and organizational accountability structures.
Analyzing failure modes in AI systems, including hallucinations, drift, and performance degradation in production environments.
Assessing broader societal implications of AI deployment, including labor market effects, misinformation, and democratic governance.
In-depth analysis of critical AI challenges and pathways forward.
Prompt Injection and the Myth of Machine Gumption TL;DR: Prompt injection keeps working because an LLM doesn’t have enforceable loyalty to its instructions; language is data and control at once, and no filter can…
Read Full PostTransformer Prowess and Sycophancy Are Not Evidence of Consciousness TL;DR: A model’s eager agreement is better explained as a low-cost transformer behavior amplified by training than as a sign of understanding or…
Read Full PostThe Gnomic Failure Mode Behind Mode Collapse TL;DR: Generative models don’t fail only by producing bad samples; they fail more dangerously when they produce convincing ones while abandoning parts of the data…
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