As organizations increasingly integrate Artificial Intelligence (AI) into their products and workflows, a new generation of risks has emerged that traditional security measures do not address. AI systems are complex, dynamic, and continuously learning. Ensuring they are secure, safe, and trustworthy is critical for innovation, compliance, and brand protection.
Frameworks such as Gartner’s AI Trust, Risk, and Security Management (TRiSM) provide a roadmap for AI governance. Aligned with these principles, HackerOne’s AI Systems Testing solutions operationalize AI governance frameworks by providing adversarial validation that bridges theory and practice. HackerOne combines human ingenuity with methodology-driven testing to uncover vulnerabilities and failure modes unique to AI.
Understanding AI Security, Safety, and Trust
Most enterprise AI incidents result not from external attacks, but from internal guideline violations, oversharing, and unintended model behavior. To secure AI effectively, it’s important to understand and test across its distinct risk surfaces. HackerOne’s offerings address both AI Security and AI Safety, which together build overall trust in AI systems.
AI Security
AI Security focuses on protecting systems from external, adversarial manipulation. These are technical exploits that target the model, its application layer, or the supporting infrastructure.
Examples include:
Prompt injection and insecure output handling
Model theft and data poisoning
Bypassing access controls to compromise plugins or data
AI Safety
AI Safety aims to prevent the model from producing unintended, harmful, or unreliable outputs. These risks typically arise from flaws in training data or from incomplete behavioral guidelines.
Examples include:
Generating toxic, biased, or illegal content
Disclosing sensitive information through hallucinations
Model misuse that leads to reputational or legal risk
By addressing both security and safety, you create trust—the confidence that your AI systems behave as intended, align with your guidelines, and remain resilient under real-world conditions.
Choosing the Right AI Testing Solution
HackerOne offers a suite of solutions designed to test and validate your AI deployments. Each solution targets a specific goal—from continuous assurance and compliance to deep adversarial simulation.
| Add-On AI/LLM Pentest | Standalone AI/LLM Pentest | AI Bug Bounty | AI Red Teaming (AIRT) |
Primary Goal | Achieve technical assurance on the AI/LLM features of traditional applications.
| Achieve compliance and technical assurance through a point-in-time security assessment. | Continuous discovery of security, safety, and abuse risks as your AI models evolve. | Simulate real-world misuse and abuse to test for safety failures and unintended behaviors. |
Best For | Finding prompt injection and jailbreaks (feature-level), unsafe/unfiltered outputs, API integration points, and contextual risks with app auth/business logic.
| Fulfilling audit-friendly technical assessments to meet compliance mandates (e.g., EU AI Act, NIST AI RMF). | Organizations with AI in production need ongoing testing to cover an evolving risk surface. | Deeply testing AI defenses against creative, objective-based attacks before or after launch. |
Duration | 2 weeks (Typical) | 2 weeks (Typical) | Continuous ("Always-on"). | 15 or 30 days. |
Approach | Lightweight coverage of tests and risks in the context of broader application pentest. | Methodical and checklist-driven, aligned to frameworks like OWASP LLM Top 10. | Creative, open bug hunting where you pay for validated results. | Creative and objective-based hunting focused on a pre-defined threat model. You pay for results. |
Participants | A focused team of 1-5 vetted application pentesters with relevant skills. | A focused team of 1-5 vetted pentesters with relevant skills. | Open to the entire HackerOne community of over 2 million researchers. | A curated community of specialized AI researchers (from 10s to 100s). |
Deliverables | Findings and methodology reported as part of the main application pentest report. | A final, audit-ready assessment report detailing vulnerabilities, fixes, and mapped compliance frameworks. | A continuous stream of validated findings, triaged submissions, and performance trends via the HackerOne platform. | A detailed report on the threat model, discovered failure modes, jailbreaks, and researcher findings. |
Next Steps
Explore the following docs to learn more about each AI Systems Testing solution: