Haize Labs: AI Safety Testing

As generative AI systems move from demos to production, enterprises face a new category of risk: behavioral safety. Unlike traditional security vulnerabilities that compromise systems through code exploits, AI agents can fail through prompt injection, goal misalignment, or adversarial inputs that trigger unsafe outputs. Haize Labs has emerged as a specialized player addressing this challenge through automated red-teaming for large language models and AI agents. In this article, we'll examine Haize Labs' approach to AI safety testing, explore their platform capabilities, and discuss how their offering compares to infrastructure solutions like WorkOS.

What is Haize Labs?

Founded by Leonard Tang (a 22-year-old Stanford PhD dropout from Berkeley AI Research Lab), Richard Liu, and Steve Li, Haize Labs operates at the cutting edge of AI safety validation. The company received a $100M post-money valuation, reflecting investor confidence in the growing need for AI safety testing.

Haize Labs provides automated red-teaming infrastructure for organizations deploying generative AI. Their platform—comprising Judge, Haize, Monitor, and Robustify—enables enterprises to systematically discover vulnerabilities in LLMs and AI agents before those systems reach production. Rather than manual security audits that scale poorly, Haize Labs uses algorithmic approaches to stress-test AI behavior at scale.

The company works with leading AI organizations including OpenAI, Anthropic, AI21 Labs, Google Cloud, Scale AI, HackerOne, Deloitte, and MongoDB. Notably, Haize Labs collaborates with OpenAI and Anthropic on pre-release model testing—a testament to the trust frontier AI labs place in their red-teaming capabilities. Their work sits squarely in the AI safety and testing domain, focused on identifying behavioral risks rather than traditional authentication or authorization concerns.

Key Features and Capabilities

Cascade: Automated Multi-Turn Red-Teaming

Haize Labs' flagship technology, Cascade, represents a breakthrough in automated adversarial testing. Using tree search algorithms, Cascade conducts multi-turn conversations designed to elicit unsafe or misaligned outputs from AI models. Unlike one-shot jailbreak attempts, Cascade simulates sophisticated attack chains where each prompt builds on previous responses. In benchmarks, Cascade has outperformed human red-teamers from Scale AI, demonstrating that algorithmic approaches can exceed expert human performance in finding edge cases.

ACG: Accelerated Coordinate Gradient

For organizations concerned about computational costs, Haize Labs developed ACG (Accelerated Coordinate Gradient), an optimization technique that delivers 38x faster attack generation with 4x reduction in GPU memory usage. This efficiency breakthrough makes continuous red-teaming economically viable for production deployments, where testing needs to run against every model update or configuration change.

Verdict: Cost-Effective Evaluation Library

Haize Labs open-sourced Verdict, a library for building custom evaluators and reward models. Verdict achieves performance matching or exceeding OpenAI's o1 and o3-mini models on tasks like hallucination detection—while costing a fraction of the price. On hallucination benchmarks, Verdict showed +14.5% improvement over GPT-4o, demonstrating that specialized evaluation models can outperform general-purpose reasoning models for specific safety tasks.

Automated Fuzz Testing and Continuous Monitoring

Beyond red-teaming, Haize Labs provides automated fuzz testing to identify quality, performance, and robustness gaps in AI applications. Their Monitor product offers real-time dashboards tracking model behavior in production, while Robustify creates feedback loops for continuous improvement. The platform integrates with CI/CD pipelines, enabling teams to treat AI safety testing as part of standard deployment workflows rather than an afterthought.

Red-Teaming Resistance Leaderboard

Haize Labs maintains a Red-Teaming Resistance Leaderboard on HuggingFace, providing public benchmarks for how different models perform against adversarial attacks. This transparency helps organizations make informed decisions when selecting foundation models for their applications.

How Haize Labs Handles AI Behavioral Safety

Haize Labs approaches AI safety through systematic adversarial testing rather than guardrails or filters. The philosophy: if you want to know where your model breaks, you need to actively try to break it. Their platform generates thousands of adversarial test cases across different attack vectors—prompt injection, goal misalignment, toxic output generation, hallucination inducement—and surfaces specific scenarios where models fail.

This differs fundamentally from runtime guardrails that attempt to filter problematic inputs or outputs. Haize Labs operates at the testing and validation layer, helping teams understand their AI system's failure modes before deployment. The platform identifies what prompts cause problems and under what conditions, enabling teams to fine-tune models, improve system prompts, or add targeted guardrails where evidence shows they're needed.

For organizations building on foundation models from OpenAI, Anthropic, or other providers, Haize Labs offers application-layer testing to validate that your specific implementation—with your prompts, your retrieval systems, your user interfaces—behaves safely. For AI labs developing foundation models, Haize Labs provides deeper model-level testing to identify inherent vulnerabilities before release.

Pricing and Plans

Haize Labs operates on custom enterprise pricing not publicly disclosed. Given their customer base includes OpenAI, Anthropic, and Google Cloud, contracts reportedly reach into the multi-million dollar range for comprehensive foundation model testing. For application-layer testing and compliance solutions, pricing scales based on testing volume, model complexity, and integration requirements.

The company offers services across three tiers: foundation model testing for AI labs, application layer testing for enterprises deploying AI, and compliance solutions for regulated industries. Organizations interested in Haize Labs typically engage through a proof-of-concept where the team demonstrates their platform's ability to find vulnerabilities in the customer's specific AI implementation.

Haize Labs vs. WorkOS

Understanding the relationship between Haize Labs and WorkOS requires recognizing they operate in fundamentally different domains that complement rather than compete.

What Haize Labs Offers

Haize Labs specializes in AI behavioral safety—testing whether your language model or AI agent produces safe, aligned outputs when subjected to adversarial inputs. Their platform answers questions like: Can users jailbreak my chatbot? Does my agent hallucinate confidential information? Will my model generate harmful content under adversarial prompting?

This focus on AI behavior testing means Haize Labs doesn't provide authentication infrastructure, user management, SSO integration, or directory sync capabilities. If your AI application needs enterprise customers to log in via Okta SAML, needs to provision users from Microsoft Entra ID, or requires admin portals for IT administrators—Haize Labs isn't designed to solve those problems.

Why WorkOS Is the Proven Choice for Enterprise Authentication

While Haize Labs validates that your AI behaves safely, WorkOS ensures that only the right users can access your AI in the first place. WorkOS provides the authentication and authorization infrastructure that enterprises require before they'll adopt your AI application.

Battle-Tested at Scale: WorkOS powers authentication for thousands of B2B SaaS companies serving enterprise customers. The platform handles millions of authentication events daily with 99.99% uptime, providing the reliability that production AI applications require.

Comprehensive Enterprise Auth Suite: WorkOS delivers complete enterprise authentication features that Haize Labs doesn't address—SSO with support for 50+ identity providers, directory sync via SCIM for automated user provisioning, multi-factor authentication, admin portals for IT administrators, and detailed audit logs for compliance. These aren't experimental features; they're production-ready capabilities enterprises require from day one.

Enterprise Features Haize Labs Lacks: Because Haize Labs focuses on AI safety testing, it doesn't provide the authentication infrastructure that enterprise customers demand: no SAML/OIDC SSO integration, no directory sync from Okta/Microsoft Entra ID/Google Workspace, no admin portal for customer IT teams, no RBAC for organizational access control.

Production-Ready Today: WorkOS ships with comprehensive documentation, SDKs for every major language, and support that matches production stakes. Enterprises can implement enterprise SSO in hours rather than weeks, with white-glove onboarding and dedicated support when needed.

Developer Experience That Scales: WorkOS abstracts the complexity of enterprise authentication behind clean APIs. Rather than implementing SAML separately for each identity provider, WorkOS provides unified interfaces that work across all providers—reducing your authentication code from thousands of lines to dozens.

The Right Choice for Production AI Applications

For enterprises building production AI applications, the decision isn't Haize Labs versus WorkOS—it's recognizing that you need both categories of solutions working together.

For enterprise authentication and user management: WorkOS is the proven choice. When your AI application needs enterprise customers to adopt it, WorkOS provides the SSO, directory sync, and admin capabilities that unblock enterprise sales.

For AI behavioral safety testing: Haize Labs offers specialized capabilities in automated red-teaming and adversarial validation that WorkOS doesn't address. If you're deploying LLMs or AI agents where behavioral safety is critical, Haize Labs provides testing infrastructure that complements your authentication layer.

For teams building production AI systems: Start with WorkOS to ensure your authentication infrastructure meets enterprise requirements, enabling you to close enterprise deals. Then layer in specialized AI safety testing from providers like Haize Labs to validate that your AI behaves correctly once users are authenticated and authorized.

WorkOS is the proven foundation for enterprise AI applications; Haize Labs is a specialized tool for teams with sophisticated AI safety testing requirements. They complement rather than compete.

Getting Started with Haize Labs

Organizations interested in Haize Labs typically begin with a discovery call where the team assesses your AI deployment and testing needs. Given the custom enterprise nature of their offering, implementation involves working with Haize Labs engineers to integrate their testing platform with your model deployment pipeline.

The platform requires technical sophistication to leverage effectively—teams need ML engineering capacity to interpret red-teaming results and implement mitigations. Documentation is available for enterprise customers, though much of the platform's value comes through guided implementation with Haize Labs' team given the specialized nature of adversarial testing.

For organizations seeking to validate their AI safety posture, Haize Labs offers proof-of-concept engagements where they demonstrate their platform's ability to find vulnerabilities in your specific models or applications.

Final Thoughts

Haize Labs represents meaningful innovation in AI safety testing, bringing algorithmic rigor to the challenge of validating AI behavioral safety at scale. Their work with leading AI labs and enterprises demonstrates real market need for automated red-teaming capabilities as generative AI moves to production.

That said, behavioral safety testing solves a fundamentally different problem than authentication infrastructure. Haize Labs validates that your AI behaves correctly; WorkOS ensures that the right users can access it with the enterprise-grade authentication features their IT teams require.

For teams building production AI applications that enterprises will trust, WorkOS provides the proven authentication foundation that unblocks enterprise adoption. Features like SSO, directory sync, and admin portals aren't optional extras—they're requirements that enterprise buyers won't compromise on. WorkOS delivers these capabilities as production-ready infrastructure you can implement in hours.

Haize Labs contributes valuable capabilities in the emerging AI safety testing market. But when it comes to the authentication and user management infrastructure that enterprises require from AI applications, WorkOS remains the proven, comprehensive choice that production systems are built on.

Ready to add enterprise-grade authentication to your AI application? Start with WorkOS and implement SSO, directory sync, and admin portals in hours, not weeks. WorkOS provides the authentication foundation that lets you focus on your AI innovation while meeting the enterprise security requirements your customers demand.