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Anthropic Safety Blog
webCredibility Rating
4/5
High(4)High quality. Established institution or organization with editorial oversight and accountability.
Rating inherited from publication venue: Anthropic
This is Anthropic's official research hub; useful as a reference index for their safety-focused publications, though individual papers within it carry more substantive content than the landing page itself.
Metadata
Importance: 62/100blog posthomepage
Summary
The Anthropic research and safety blog aggregates publications, technical reports, and commentary from Anthropic's research teams covering AI safety, alignment, interpretability, and responsible deployment. It serves as a central hub for Anthropic's public-facing scientific and policy work. Content spans empirical safety research, model evaluations, and foundational alignment topics.
Key Points
- •Central repository for Anthropic's published research across safety, alignment, interpretability, and capabilities topics
- •Includes technical papers, model cards, policy briefs, and blog posts from Anthropic researchers
- •Covers core safety themes: constitutional AI, RLHF, red-teaming, and evaluation methodologies
- •Reflects Anthropic's mission-driven approach to developing AI safely while remaining at the frontier
- •Useful for tracking ongoing developments in industry-led AI safety research
Cited by 1 page
| Page | Type | Quality |
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| Persuasion and Social Manipulation | Capability | 63.0 |
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# Research
Our research teams investigate the safety, inner workings, and societal impacts of AI models – so that artificial intelligence has a positive impact as it becomes increasingly capable.
Research teams: [Alignment](https://www.anthropic.com/research/team/alignment) [Economic Research](https://www.anthropic.com/research/team/economic-research) [Interpretability](https://www.anthropic.com/research/team/interpretability) [Societal Impacts](https://www.anthropic.com/research/team/societal-impacts)
### Interpretability
The mission of the Interpretability team is to discover and understand how large language models work internally, as a foundation for AI safety and positive outcomes.
### Alignment
The Alignment team works to understand the risks of AI models and develop ways to ensure that future ones remain helpful, honest, and harmless.
### Societal Impacts
Working closely with the Anthropic Policy and Safeguards teams, Societal Impacts is a technical research team that explores how AI is used in the real world.
### Frontier Red Team
The Frontier Red Team analyzes the implications of frontier AI models for cybersecurity, biosecurity, and autonomous systems.
[**What 81,000 people want from AI**](https://www.anthropic.com/81k-interviews)
[We invited Claude.ai users to share how they use AI, what they dream it could make possible, and what they fear it might do. Nearly 81,000 people participated—the largest and most multilingual qualitative study of its kind. Here's what we found.](https://www.anthropic.com/81k-interviews)
[PolicyDec 18, 2025\\
**Project Vend: Phase two** \\
In June, we revealed that we’d set up a small shop in our San Francisco office lunchroom, run by an AI shopkeeper. It was part of Project Vend, a free-form experiment exploring how well AIs could do on complex, real-world tasks. How has Claude's business been since we last wrote?](https://www.anthropic.com/research/project-vend-2) [InterpretabilityOct 29, 2025\\
**Signs of introspection in large language models** \\
Can Claude access and report on its own internal states? This research finds evidence for a limited but functional ability to introspect—a step toward understanding what's actually happening inside these models.](https://www.anthropic.com/research/introspection) [AlignmentFeb 3, 2025\\
**Constitutional Classifiers: Defending against universal jailbreaks** \\
These classifiers filter the overwhelming majority of jailbreaks while maintaining practical deployment. A prototype withstood over 3,000 hours of red teaming with no universal jailbreak discovered.](https://www.anthropic.com/research/constitutional-classifiers) [AlignmentDec 18, 2024\\
**Alignment faking in large language models** \\
This paper provides the first empirical example of
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