Google has expanded its bug bounty program to explicitly include AI systems and connected tools, covering Gemini models and related integrations. Researchers can earn up to $30,000 for reporting high impact vulnerabilities. The change underscores a growing industry focus on AI model safety and coordinated vulnerability disclosure to reduce real world risk.

Why AI needs dedicated bug bounty coverage

AI systems and model based integrations change the threat landscape in two core ways. First, model behavior opens new exploit classes such as prompt injection attacks that manipulate outputs rather than code. Second, models expand the attack surface by linking user data, cloud services, and on device hardware into a single workflow. These factors make AI bug bounty programs a practical tool for LLM security testing and continuous assurance.

Key details

• Reward cap: Eligible reports can receive rewards of up to $30,000 for high impact bugs.
• Scope: The program covers Gemini models and connected tools, moving beyond traditional web and cloud assets to include model behavior and integrations.
• Vulnerability types in focus: prompt injection attacks, smart home exploits that control physical devices, and account takeover methods that abuse AI workflows.
• Disclosure model: Google is encouraging coordinated vulnerability disclosure and third party testing under its Google Vulnerability Reward Program VRP.

Plain language definitions

• Prompt injection: Crafting input that tricks a model into ignoring safety guardrails or producing unsafe outputs.
• Smart home exploit: A vulnerability that lets attackers manipulate devices such as locks or cameras through software or model links.
• Account takeover: When attackers gain unauthorized access to accounts, sometimes using AI assisted social or technical techniques.

Implications for organizations

The expansion matters on technical, organizational, and regulatory levels. From a technical perspective, model based integrations require new defenses that combine model level guardrails with engineering controls. From an organizational perspective, paying for external research is a cost effective way to detect high risk issues early. And from a regulatory perspective, formal bug bounty programs help vendors meet rising expectations for independent testing and transparent reporting.

Technical guidance

• Treat models as critical infrastructure. Any third party model or integration should be assessed like other mission critical systems.
• Monitor and log model inputs and outputs to detect anomalous patterns that could indicate prompt injection or model exfiltration.
• Combine model level safety checks with access controls, rate limits, and audit trails to reduce attack surface.

Organizational steps

• Contract for security with vendors. Require disclosure timelines and proof of external testing as part of procurement.
• Build triage and patching workflows that include model behavior analysis and red teaming focused on LLM security testing.
• Invest in staff skills for adversarial prompt engineering and model monitoring to maintain continuous assurance.

Market and regulatory signals

Formalizing an AI bug bounty program signals that vendors are taking trust and safety seriously. Enterprises will likely view such programs as part of a vendor trustworthiness checklist. Regulators and standards bodies are also pushing for independent testing and clearer risk assessment practices, and coordinated vulnerability disclosure is becoming an expectation for production deployments.

A brief expert observation

Independent reporting and incentivized discovery are an efficient way to find complex, model based flaws that internal testing may miss. Open bug bounty programs align incentives between vendors and security researchers and create a faster path to safer AI in production.

Conclusion and call to action

Google offering up to $30,000 for AI related bug discoveries is a notable step in the evolution of AI security. For businesses the advice is clear. Assume wider attack surfaces, demand vendor accountability, log and monitor model interactions, and prepare teams to work with external researchers. Expect more vendors to follow and for disclosure norms to evolve as AI systems move deeper into enterprise workflows.