Targeted, time-bound offensive testing for AI models
Call on a skilled researcher community to identify and mitigate unintended behaviors and vulnerabilities in AI systems.
Define scope & risk priorities
Identify which AI models and systems are in scope and establish key risk and safety priorities.
- Determine the AI models and systems vulnerable to attack.
- Focus on specific risks, such as model theft, bias, and security concerns like OWASP Top 10 for LLMs.
- Align the testing scope with your organization's risk management strategy.
Design a tailored threat model
Create a threat model and test plan that addresses your AI risk priorities.
- Conduct thread modeling to assess how adversaries might target your AI.
- Develop a tailored testing plan that targets specific vulnerabilities and risks.
- Ensure the plan covers AI safety and security threats such as adversarial attacks and model degradation.
Engage experts & execute testing
Source skilled researchers and manage the testing process with ongoing support.
- Select researchers experienced in AI vulnerabilities like prompt engineering and adversarial inputs.
- Manage testing and integrate findings into your security workflows.
- Leverage solutions architects for guidance throughout testing period.
Centralize reporting and remediation
Receive actionable reports in the HackerOne Platform to ensure effective remediation.
- Capture detailed findings on vulnerabilities, paired with clear prioritized recommendations.
- Use the centralized platform to track, manage, and validate remediation efforts.
- Ensure all issues are resolved to secure your AI systems and prevent future risks.
Security advisory services
Manage and scale your program with best practices and insights from experts in cyber risk reduction. Our solutions architects help tailor your program—from custom workflows to KPIs for measuring program success.
Hai: Your HackerOne GenAI copilot
Our in-platform AI copilot provides an immediate understanding of your security program so you can make decisions and deliver fixes faster. Effortlessly translate natural language into queries, enrich reports with context, and use platform data to generate recommendations.
