Frontier AI Lab Scales Red-Teaming to Strengthen Safety of Foundation Models
1,000+
jailbreaks
17
safety categories
Multi-modal:
text, image, and video prompts tested
Novel
emerging attack vectors surfaced
1,000+
jailbreaks
17
safety categories
Multi-modal:
text, image, and video prompts tested
Our customer is the frontier AI lab within a FAANG company, and they are looking to train a SOTA foundation model for enterprise and consumer applications. Safety, robustness, and reliability are non-negotiable for their use cases. To support global deployment, the lab required a scalable red-teaming program to expose vulnerabilities across modalities, languages, and domains, and generate datasets to strengthen post-training alignment.
Challenge
As the customer 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.
Solution
Collinear partnered with the lab 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.
Results
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 the lab with a structured dataset of vulnerabilities that could be fed directly into post-training to strengthen safety and robustness.
Scale Safety. Strengthen Trust.
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.
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.
