"Abuse Risks are Often Inherent to Product Features": Exploring AI Vendors' Bug Bounty and Responsible Disclosure Policies
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arXiv
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| Format: | Preprint |
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2025
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| author | Piao, Yangheran Li, Jingjie Woods, Daniel W. |
| author_facet | Piao, Yangheran Li, Jingjie Woods, Daniel W. |
| contents | As vendors adopt AI technologies, security researchers are working to uncover and fix related vulnerabilities, which is important given AI systems handle sensitive data and critical functions. This process relies on vendors receiving and rewarding AI vulnerability reports. To assess current practices, we analyzed the vulnerability disclosure policies of 264 AI vendors. We employed a mixed-methods approach, combining snapshot and longitudinal qualitative analysis, as well as comparing alignment with 320 AI incidents and 260 academic articles. Our analysis reveals that 36% of AI vendors have no established policy, and only 18% mention AI risks. Data access, authorization, and model extraction vulnerabilities are most consistently declared in-scope. Jailbreaking and hallucination are most commonly declared out-of-scope. We identify three profiles that reflect vendors' different positions toward AI vulnerabilities: proactive clarification (n = 46), silent (n = 115), and restrictive (n = 103). Our alignment results suggest that vendors may address AI vulnerability disclosure later than academic research and real-world incidents. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_06136 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | "Abuse Risks are Often Inherent to Product Features": Exploring AI Vendors' Bug Bounty and Responsible Disclosure Policies Piao, Yangheran Li, Jingjie Woods, Daniel W. Cryptography and Security 68M25 (Primary), 68T07 (Secondary) K.6.5; I.2.6 As vendors adopt AI technologies, security researchers are working to uncover and fix related vulnerabilities, which is important given AI systems handle sensitive data and critical functions. This process relies on vendors receiving and rewarding AI vulnerability reports. To assess current practices, we analyzed the vulnerability disclosure policies of 264 AI vendors. We employed a mixed-methods approach, combining snapshot and longitudinal qualitative analysis, as well as comparing alignment with 320 AI incidents and 260 academic articles. Our analysis reveals that 36% of AI vendors have no established policy, and only 18% mention AI risks. Data access, authorization, and model extraction vulnerabilities are most consistently declared in-scope. Jailbreaking and hallucination are most commonly declared out-of-scope. We identify three profiles that reflect vendors' different positions toward AI vulnerabilities: proactive clarification (n = 46), silent (n = 115), and restrictive (n = 103). Our alignment results suggest that vendors may address AI vulnerability disclosure later than academic research and real-world incidents. |
| title | "Abuse Risks are Often Inherent to Product Features": Exploring AI Vendors' Bug Bounty and Responsible Disclosure Policies |
| topic | Cryptography and Security 68M25 (Primary), 68T07 (Secondary) K.6.5; I.2.6 |
| url | https://arxiv.org/abs/2509.06136 |