Anthropic has engaged in an ongoing partnership with the US Center for AI Standards and Innovation (CAISI) and the UK AI Security Institute (AISI), government bodies focused on improving AI system security. This collaboration, which began as initial consultations, evolved to provide CAISI and AISI teams with access to Anthropic's systems at various stages of model development. The partnership allowed for continuous testing of these systems, including Constitutional Classifiers on models such as Claude Opus 4 and 4.1, prior to their deployment.
Key Points
- Anthropic collaborated with the US Center for AI Standards and Innovation (CAISI) and the UK AI Security Institute (AISI).
- Government red-teamers tested Constitutional Classifiers on Claude Opus 4 and 4.1.
- The collaboration identified vulnerabilities, including prompt injection attacks and sophisticated universal jailbreaks.
- CAISI and AISI helped strengthen Anthropic's security approach, including evidence requirements and deployment monitoring.
- Anthropic provided government red-teamers with comprehensive model access, including API access, model weights, and internal documentation.
- The testing led to improvements in safeguard architecture and detection systems for obfuscated harmful content.
Context
According to Anthropic, government bodies like CAISI and AISI bring expertise in national security areas such as cybersecurity and threat modeling, which complements their machine learning knowledge. This allows them to evaluate specific attack vectors and defense mechanisms. Anthropic states that working with independent external experts to identify vulnerabilities is a core part of its Safeguards approach.
Why It Matters
This collaboration demonstrates how AI developers can engage with government research and standards bodies to enhance the safety and security of AI models. It highlights a practice for identifying and addressing risks through public-private partnerships, which can inform how other companies approach external evaluations of their AI systems.
What To Do
- Note the types of vulnerabilities identified, such as prompt injection and obfuscation attacks.
- Observe how comprehensive model access and iterative testing contributed to vulnerability discovery.
- Consider the benefits of engaging with independent external experts for AI system evaluation.
- Watch for further guidance or frameworks from CAISI and AISI regarding AI security standards.
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