Bitcoin Red Team Founder Says Chinese AI Shift ‘Guts Me’

A Bitcoin security researcher says he lost access to OpenAI’s Trust & Cyber tooling while working on ongoing red-team scans, forcing him to revert to alternative AI options for future analysis. The episode underscores a broader worry shared by parts of the crypto defense community: that the most capable AI systems may not be readily available to those trying to harden public code against cyber threats.

In an X post on Tuesday, AnchorWatch CEO Rob Hamilton said he began integrating OpenAI’s Trust & Cyber capabilities into his Bitcoin Red Team efforts on Saturday. He later reported that his access was restricted the following morning, prompting him to switch back to using Chinese open-source models for vulnerability research. Hamilton framed the change as a practical necessity for maintaining defensive work rather than a preferred approach.

Key takeaways

• Hamilton says access to OpenAI’s Trust & Cyber capabilities was restricted after he started using it for Bitcoin Red Team research.
• He plans to continue scanning Bitcoin-related repositories using Chinese open-source AI models rather than relying on the previously integrated tooling.
• Bitcoin Red Team’s approach combines AI-assisted scanning with human review across hundreds of open-source repositories.
• The incident echoes wider concerns from crypto leaders that “frontier” AI access remains limited despite rising cyber risk.

A sudden access restriction changes the research workflow

Hamilton’s post describes a short integration window: after beginning to use OpenAI’s Trust & Cyber tools for Bitcoin Red Team on Saturday, he said he was prevented from continuing the investigation after access was restricted the next day. In his view, the restriction limited not only the ability to evaluate existing code changes but also to check whether additional issues remained undiscovered.

Hamilton also characterized the situation as a policy bottleneck, implying that defensive teams are constrained by rules that attackers can bypass. He argued that “intelligence is unrestricted” for actors who pursue harm, while defenders conducting “harm reduction” are left without comparable tooling. The core point is less about the specific model choice and more about continuity: red-team work depends on sustained access to iterative analysis tools as scans evolve and new leads emerge.

Why this matters for Bitcoin security testing

Bitcoin Red Team is described as a volunteer effort using AI tools and human review to examine a large number of Bitcoin-related open-source repositories for vulnerabilities. According to the account referenced in Hamilton’s post, the work has been particularly active in the wake of major wallet security incidents.

That timing is important because defenders often need rapid, repeatable workflows to assess code changes across a sprawling ecosystem. When an AI tool is removed midstream, it can slow down verification, increase manual effort, or force researchers to restart parts of their process with different systems. Hamilton’s statement suggests the restriction wasn’t merely a temporary inconvenience—it affected his ability to continue investigating code updates and to explore whether other weaknesses might be present.

His comment also reflects a recurring pattern in security research: tools that speed up initial discovery are only as useful as the ability to keep investigating after early findings. If the process is cut short, the risk of leaving unresolved vulnerabilities rises, especially in open-source environments where issues may be subtle and scattered across multiple repositories.

Escalating threat pressure and limited AI access

The episode fits into a larger debate inside crypto about who gets access to advanced AI capabilities. Earlier coverage referenced in the article notes that crypto executives told Cointelegraph last month that many major firms were still waiting to obtain powerful new AI models to help secure their code against escalating cyber threats, with only a select few having been able to get access.

Bringing Hamilton’s account into that context, the risk for the broader sector is not only that attackers will improve their methods, but that defenders may not be able to match speed and depth. If the most effective tools are restricted, available only to a narrow set of organizations, or subject to sudden changes in access policy, the defense pipeline may become uneven.

Hamilton’s complaint is also notable for its emphasis on “sufficient” code changes. In vulnerability research, it is not enough to identify a potential bug; teams also need to confirm that patches address the underlying issue and do not introduce new problems. Cutting off access at the point where verification is needed is therefore more damaging than removing a tool at the early scanning stage.

What readers should watch next

The immediate story is a researcher switching back to Chinese open-source AI models after reporting restricted access to OpenAI’s Trust & Cyber capabilities. Going forward, observers will likely focus on whether Bitcoin Red Team can maintain its scan velocity and depth without the previously used tools, and whether other crypto security teams report similar access volatility as they try to use frontier AI for defensive purposes.