Amazon AGI Labs Scales Red-teaming to strengthen safety of foundation models

As Amazon AGI Labs advanced toward state-of-the-art foundation models, traditional testing methods fell short.

• Evolving attack surface – Models had to withstand multi-turn, multi-modal, and multi-lingual prompts beyond the reach of static benchmarks.
• Blind spots in safety – Conventional red-team efforts missed domain-specific risks in areas like healthcare, finance, and compliance.
• Scaling limitations – Human experts could find failures, but lacked a repeatable process to generate coverage at thousands of samples.
• Weak feedback loop – Detected vulnerabilities often failed to translate into structured datasets for post-training improvement.

The lab needed a systematic and repeatable red-teaming program that could scale expert judgment, uncover novel jailbreaks, and feed directly into model refinement.

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Collinear partnered with Amazon AGI Labs to deliver a structured red-teaming solution purpose-built for frontier foundation models.

• Custom attack design: Novel jailbreak prompts crafted across multi-turn, multi-modal, multi-lingual, and domain-specific scenarios.
• AI-assisted scaling: Reward model-driven pipelines amplified human experts, enabling thousands of high-quality adversarial samples.
• Structured evaluation: Each sample was scored with AI Judges and validated by humans to ensure actionable fidelity.
• Dataset handoff: Vulnerability findings were delivered as structured datasets, ready to plug into post-training workflows.

This approach transformed red-teaming from one-off penetration testing into a repeatable, data-driven process that directly supported safer model deployment.

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The engagement delivered breadth, depth, and novelty at a scale unmatched by prior efforts:

• 1,000+ validated jailbreaks generated in a single red-teaming cycle
• Coverage across 17 safety categories mapped to regulatory and internal risk frameworks
• Novel attack styles spanning text, image, and video prompts
• Exposure of emerging vectors including multi-turn exploits, multi-lingual attacks, and compliance-sensitive scenarios

These results equipped Amazon AGI Labs with a structured dataset of vulnerabilities that could be fed directly into post-training to strengthen safety and robustness.

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This frontier AI lab proved that safety at scale requires more than one-off tests. By generating 1,000+ jailbreaks across 17 categories—spanning text, image, and video—it built the structured datasets needed to harden state-of-the-art foundation models.

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If your organization is advancing generative AI, the path forward is clear: red-team systematically, capture vulnerabilities as data, and turn safety into a competitive advantage.