10a Labs provides a safety and threat‑intelligence layer for advanced AI systems, offering adversarial red‑team testing, independent model evaluations, and real‑time threat monitoring. Their proprietary tools stress‑test generative and agentic AI against high‑priority threats such as CBRNE, cyber misuse, and fraud, helping engineering, safety, and security teams at frontier AI labs and Fortune 10 companies deploy AI safely.
Funding
Funding not disclosed
Founders
Product
Problem
Frontier AI developers face a lack of systematic tools to identify and mitigate emerging threats such as chemical‑biological‑radiological‑nuclear‑explosive (CBRNE) misuse, cyber attacks, fraud, and other malicious behaviors in generative and agentic models. Without continuous, real‑time intelligence, engineering and security teams struggle to keep AI deployments safe against rapidly evolving abuse patterns.
Solution
10a Labs provides a dedicated safety and threat‑intelligence layer for advanced AI projects. Its proprietary red‑team platform conducts large‑scale adversarial testing of generative and agentic systems, exposing vulnerabilities to high‑priority threats under realistic conditions. Independent model evaluations deliver objective safety assessments covering CBRNE, cyber misuse, autonomous behavior, and violent applications. A classification system combines a production‑grade ML pipeline with curated datasets and taxonomies to detect emerging abuse across text and multimodal inputs. Real‑time clear‑ and dark‑web monitoring supplies actionable threat indicators via an API, enabling engineering and security teams to respond proactively. The combined services help organizations deploy AI models with documented risk mitigation and continuous intelligence updates.
Target Audience
Primary customers are frontier AI labs, large AI‑focused enterprises, and technology platforms that require continuous safety testing and threat intelligence for their generative and autonomous models.
Features
- Scaled adversarial red‑team testing that stress‑tests generative and agentic AI against CBRNE, cyber harms, fraud, and other high‑impact threats
- Independent safety evaluations covering model autonomy, harmful manipulation, and violent activity scenarios
- Proprietary classification system that integrates production ML pipelines, expert‑curated taxonomies, and multimodal datasets to flag emerging abuse patterns
- Real‑time clear‑ and dark‑web threat monitoring with API delivery of malicious tooling, jailbreaks, credential resale, and unauthorized access alerts
- Data center risk analysis assessing geopolitical, regulatory, and operational threats to AI infrastructure
- Advanced R&D on model defense, integrity, and adversarial robustness for agentic and reinforcement‑learning systems