Anthropic is developing an open-source tool that could expose AI companies secretly using Claude to generate responses while claiming the answers came from their own models.
The system, called FlashProve, uses cryptographic technology to verify which AI model produced a response. Although Anthropic has not explicitly linked the project to Chinese AI companies, it could help address practices the company has previously accused Chinese competitors of using.
In September 2026, Anthropic accused Moonshot AI of secretly routing Kimi users’ requests through Claude and presenting the responses as Kimi’s own. The company has also reported multiple cases of Chinese AI laboratories allegedly extracting Claude’s capabilities to train competing models.
However, FlashProve is designed to verify which model generated an output, not determine whether another model was trained using Claude’s responses.
Chinese AI Companies Accused
According to Anthropic’s September 2026 report, Moonshot AI secretly forwarded nearly 300,000 Kimi customer requests to Claude over ten days, primarily using its Opus models.
Anthropic alleged that Moonshot accessed Claude through 5,380 fraudulent accounts, mostly appearing to operate from Singapore and Japan. It also accused the company of saving conversations and extracting Claude’s reasoning data to improve its own models.
Earlier, in February, Anthropic accused DeepSeek, Moonshot, and MiniMax of conducting large-scale model-distillation campaigns involving more than 16 million exchanges through approximately 24,000 fraudulent accounts.
Model distillation involves using responses from a more capable AI system to train another model. Although the technique has legitimate applications, Anthropic says unauthorized extraction of Claude’s capabilities violates its terms.
In September, Anthropic also accused Alibaba, Zhipu, and Xiaomi of similar activities and alleged that DeepSeek had secretly forwarded some customer requests to Claude.
These remain Anthropic’s allegations rather than independently established findings.
How Anthropic’s FlashProve Works
Anthropic introduced FlashProve on November 2, 2026, as a system for provable inference, allowing users to mathematically verify which AI model generated a response.
Currently, users generally have to trust companies when they claim a particular model produced an answer.
For example, an AI provider could secretly replace an expensive model with a cheaper alternative to reduce costs. Alternatively, it could use a more powerful model during benchmark testing to make its own system appear better.
FlashProve aims to prevent such misrepresentation through cryptographic verification.
To use the system, AI companies would publish a unique cryptographic fingerprint associated with each model.
When the model generates a response, the provider can produce mathematical proof linking that output to the published fingerprint. Users can then verify whether the response came from the advertised model.
If a different model generated the response, the verification should fail.
The approach is similar to cryptographic email signatures, which help confirm that messages came from their claimed senders.
AI Companies Won’t Need to Reveal Their Models
One major advantage of FlashProve is that companies can verify their models without revealing their internal weights.
Model weights are the numerical values that determine how an AI system processes information and generates responses. Companies such as Anthropic and OpenAI generally keep the weights of their leading models private.
FlashProve allows providers to publish cryptographic fingerprints without exposing this proprietary information.
The system is also designed to support lightweight verification on local devices, although generating the proofs requires considerably more computing power.
Participation would require AI providers to publish the necessary fingerprints and generate proofs.
FlashProve Could Also Detect Misleading AI Claims
Beyond identifying which model generated a response, FlashProve could help verify claims made by companies using AI for professional tasks.
For example, a developer might claim that an advanced cybersecurity model scanned its software for vulnerabilities, while another company might advertise that a powerful AI system reviewed its business plan.
FlashProve could provide mathematical evidence confirming which model performed the task.
It could also help researchers verify the models used during benchmark testing, making it harder for companies to secretly substitute more capable systems.
However, proving a model’s identity does not guarantee that its answers or conclusions are accurate.
Anthropic Tests FlashProve on a 400-Billion-Parameter Model
Anthropic has demonstrated FlashProve using Meta’s Llama 4 Maverick, which contains 400 billion total parameters.
Using four-bit quantized weights and a single Nvidia H100 GPU, the system generated proofs at approximately 110 tokens per second.
For a request containing 1,024 tokens, the proof was approximately 19MB and could be verified on a CPU within seconds.
However, proof generation required approximately 70 to 95 times more GPU computation than comparable inference using vLLM.
This additional computing cost remains a major obstacle to widespread adoption.
FlashProve Is Open Source but Still Experimental
Anthropic has released FlashProve’s technical specification, mathematical research, and working code on GitHub under an open-source license.
The company says the project is still in development and has limitations involving model computations, supported output types, and the relationship between its optimized implementation and formally verified protocol.
Anthropic has not announced plans to deploy FlashProve in Claude or any other commercial AI service.
Instead, researchers are making the technology available so others can study, test, and improve it.
If successfully developed and adopted, FlashProve could make AI companies more accountable for claims about which models power their services.
However, it would not independently prove unauthorized model distillation or identify the training data used to build competing AI systems.
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