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Anthropic Frontier Threats Assessment (2023)
webCredibility Rating
4/5
High(4)High quality. Established institution or organization with editorial oversight and accountability.
Rating inherited from publication venue: Anthropic
Published by Anthropic in 2023, this piece is an important industry reference for how leading AI labs approach catastrophic risk evaluation and informs ongoing debates about pre-deployment safety testing standards and responsible scaling policies.
Metadata
Importance: 72/100organizational reportprimary source
Summary
Anthropic describes their approach to red teaming frontier AI models for catastrophic risks, with particular focus on biological, chemical, nuclear, and radiological (CBRN) threats. The piece outlines methodologies for assessing whether advanced AI could provide meaningful 'uplift' to bad actors seeking to cause mass harm. It represents an early industry effort to systematize safety evaluation for the most severe potential misuse scenarios.
Key Points
- •Focuses on evaluating whether frontier AI models could meaningfully assist in creating weapons of mass destruction, especially bioweapons.
- •Introduces the concept of 'uplift' — whether AI provides significant additional capability beyond what bad actors could access otherwise.
- •Describes structured red teaming methodology involving domain experts to probe model capabilities for catastrophic misuse.
- •Argues that frontier threat assessment requires specialized evaluators with subject-matter expertise in CBRN domains.
- •Positions proactive safety evaluation as a responsibility of frontier AI labs before deployment decisions are made.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| Bioweapons Attack Chain Model | Analysis | 69.0 |
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Announcements
# Frontier Threats Red Teaming for AI Safety
Jul 26, 2023
“Red teaming,” or adversarial testing, is a recognized technique to measure and increase the safety and security of systems. While [previous Anthropic research](https://www.anthropic.com/research/red-teaming-language-models-to-reduce-harms-methods-scaling-behaviors-and-lessons-learned) reported methods and results for red teaming using crowdworkers, for some time, AI researchers have noted that AI models could eventually obtain capabilities in areas relevant to national security. For example, researchers have [called](https://cdn.openai.com/papers/gpt-4-system-card.pdf) to [measure](https://arxiv.org/abs/2305.15324) and [monitor](https://arxiv.org/abs/2108.12427) [these risks](https://938f895d-7ac1-45ec-bb16-1201cbbc00ae.usrfiles.com/ugd/938f89_74d6e163774a4691ae8aa0d38e98304f.pdf), and have [written](https://arxiv.org/abs/2306.03809) [papers](https://arxiv.org/abs/2304.05332) with evidence of risks. Anthropic CEO Dario Amodei also highlighted this topic in [recent Senate testimony](https://www.judiciary.senate.gov/committee-activity/hearings/oversight-of-ai-principles-for-regulation). With that context, we were pleased to advocate for and join in commitments announced at the White House on July 21 that included “internal and external security testing of \[our\] AI systems” to guard against “some of the most significant sources of AI risks, such as biosecurity and cybersecurity.” However, red teaming in these specialized areas requires intensive investments of time and subject matter expertise.
In this post, we share our approach to “frontier threats red teaming,” high level findings from a project we conducted on biological risks as a test project, lessons learned, and our future plans in this area.
Our goal in this work is to evaluate a baseline of risk, and to create a repeatable way to perform frontier threats red teaming across many topic areas. With respect to biology, while the details of our findings are highly sensitive, we believe it’s important to share our takeaways from this work. In summary, working with [experts](https://www.gryphonscientific.com/), we found that models might soon present risks to national security, if unmitigated. However, we also found that there are mitigations to substantially reduce these risks.
We are now [scaling up this work](https://jobs.lever.co/Anthropic/8f565d59-8831-443a-b72a-cb9ef8ae06b2) in order to reliably identify risks and build mitigations. We believe that improving frontier threats red teaming will have immediate benefits and contribute to [long-term AI safety](https://www.anthropic.com/news/core-views-on-ai-safety). We have been sharing our findings with government, labs, and other stakeholders, and we’d like to see more independent grou
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