As artificial intelligence systems quickly spread into daily life and business, questions about how to keep these systems reliable and secure have grown more urgent. Organizations and security researchers are now searching for a legal and practical balance that allows for open vulnerability testing of AI models while protecting the interests of both companies and investigators. People are increasingly aware that trust in technology depends partly on the freedom to uncover flaws, and pressure is mounting on industry leaders to clarify the rules of engagement for researchers working to improve AI safety.
Published statements and prior coverage indicate the concept of protecting “good faith” hackers in the cybersecurity field is not new. The Department of Justice’s 2022 decision to shield independent security researchers—provided their intentions are constructive—received broad attention and marked a turning point for vulnerability research. Still, attempts to extend such protections specifically to artificial intelligence, where potential social impact is vast, have generally lagged behind the pace of AI’s adoption. Major platforms like OpenAI and Anthropic have remained careful about how and when outside researchers can engage with their systems, often introducing structured—even restrictive—programs and rules.
What Is the Good Faith AI Research Safe Harbor?
HackerOne has introduced its Good Faith AI Research Safe Harbor framework, aiming to extend legal protections for security researchers who identify vulnerabilities in AI platforms. This initiative seeks to broaden the legal freedom offered to good faith researchers by the Department of Justice in traditional cybersecurity to also include AI contexts. The company encourages participating organizations to display a “banner” on their HackerOne profile, which reflects their commitment to refrain from legal action and to support researchers if third-party claims arise.
How Do Major AI Companies Handle Vulnerability Disclosure?
Leading AI firms have taken distinct approaches to enabling external scrutiny. OpenAI conducts its own selection of “red team” researchers through a controlled application process. These engagements are closely managed and limited in both scale and time commitment. Similarly, Anthropic enforces policies requiring researchers to limit their activities to what is needed to confirm a vulnerability, and to coordinate on disclosure timing.
Why Are Protections for AI Security Researchers Important Now?
The rapidly evolving nature of AI creates uncertainty for security researchers, especially when legal safeguards are inconsistent. Ilona Cohen, chief legal and policy officer at HackerOne, commented,
“Since AI systems are essentially deploying a lot faster than any of the governance or legal frameworks can keep up, that creates some risk … for all of us when people are reluctant to do testing of AI systems.”
She added,
“It doesn’t necessarily apply to all AI research,”
highlighting that existing Department of Justice guidance, while helpful for cybersecurity, does not cover the full breadth of work needed in the AI sector.
Despite increased awareness and earlier regulatory advances for traditional cybersecurity, AI still lacks clear, sector-wide practices for responsible vulnerability discovery and disclosure. This gap has prompted platforms like HackerOne to urge industry adoption of similar safe harbor measures, although major companies such as OpenAI and Anthropic have thus far preferred individual, internalized approaches. Structured, shared policies remain a work in progress, with the technology outpacing both laws and governance models for digital safety.
A practical consideration for researchers and companies is that these protections are meant to encourage responsible investigation by removing much of the legal uncertainty that could otherwise slow down or prevent important discoveries. By signaling support for “good faith” research, organizations may strengthen public trust, deter misuse, and make it safer for experts to report system weaknesses. As the details of vulnerability management and disclosure are debated, companies and researchers alike can benefit from clear, consistent policies that acknowledge the unique risks and stakes involved in AI-driven products.
