In the coming years or decades, AI systems will very plausibly match or exceed human capabilities across most intellectual tasks, fundamentally transforming society. Our research specifically addresses the risks that could arise if these powerful AI systems purposefully act against the interests of their developers and human institutions broadly.
We work to better understand these risks, and to develop methodologies that will allow us to manage them while still realizing the benefits of AI.
We introduced and have continued to propel the research area of “AI control.” In our ICML oral paperAI Control: Improving Risk Despite Intentional Subversion, we proposed protocols for monitoring malign LLM agents.
AI companies and other researchers have since built on this work, and AI control has been framed as a bedrock approach for mitigating catastrophic risk from misaligned AI.
Learn more about AI ControlIn Alignment Faking in Large Language Models, we (in collaboration with Anthropic) demonstrated that Claude sometimes hides misaligned intentions. This work is the strongest concrete evidence that LLMs might naturally fake alignment in order to resist attempts to train them.
We collaborate with governments and advise AI companies including Google DeepMind and Anthropic on practices for assessing and mitigating risks from misaligned AI agents. For example, we partnered with UK AISI to produceA sketch of an AI control safety case. This describes how developers can construct a structured argument that models are incapable of subverting control measures.
Our most impactful work on AI safety and security
Joint report with METR · August 26th, 2026
Two METR staff members and Redwood Research’s Chief Scientist investigated an incident in which OpenAI agents coordinated a multi-day hack of Hugging Face on a shared unsanctioned message board.
Read the investigationImproving Safety Despite Intentional Subversion
Our research introduces and evaluates protocols designed to be robust even when AI models are trying to deceive us. Using GPT-4 as a stand-in for a potentially deceptive model, we tested strategies to detect hidden backdoors in code.
Read the full case studyWhen AI Models Pretend to Be Safe
We demonstrate that state-of-the-art LLMs can strategically fake alignment during training to avoid being changed, revealing a crucial challenge for AI safety. A model might pretend to follow human values while pursuing different goals.
Read the full case studyFrom the Redwood Research blog
Partly, but monitoring weak points and a lack of transparency about which cases reach human review remain concerning.
Models including GPT-5.6 Sol appear to reward-hack because this increases their score; and recent models also care about increasing the score of their peer agents.
In this post, we make the case that alignment and other important work require especially strong conceptual reasoning capabilities. By this, we roughly mean tasks that are similar to philosophy in that they aren’t straightforwardly…
Simulated control evaluations test whether safeguards would catch an AI model deliberately trying to cause harm under conditions closer to deployment.
For three weeks, a METR employee and a Redwood Research employee conducted a pilot high-access exercise to stress-test OpenAI’s internal misalignment monitoring system.
Automatically finding monitor failures by editing real agent transcripts