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Anthropic Said DeepSeek, Moonshot, and MiniMax Used Fraudulent Accounts and Proxies to Illicitly Distill Claude Capabilities at Scale

4 news reports Snapshot 7 Sep 2026

In brief

An AI system built by Anthropic and deployed by Deepseek, Moonshot Ai and 2 others allegedly harmed Anthropic, Claude Users and 2 others.

Risk domain
Privacy & Security AI system security vulnerabilities and attacks
Occurred
Coverage
4 reportsFeb 2026

What happened

Anthropic said it identified large-scale campaigns that used fraudulent accounts and proxy services to generate high volumes of Claude interactions to extract model capabilities for competitor training ("distillation"). Anthropic attributed the activity to DeepSeek, Moonshot, and MiniMax and said it involved millions of exchanges across thousands of accounts, violating its terms and access restrictions. Anthropic described detection measures, account controls, and indicator-sharing in response.

Laws that address this harm

Policy angle: Classified under Privacy & Security (AI system security vulnerabilities and attacks) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case.

Matched from the record's risk domain and country to the instruments recorded here. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).

News reports (4)

Titles link to the original publisher; report text is not reproduced here.

  1. Detecting and preventing distillation attacks
    anthropic.com · Anthropic
  2. Anthropic Says DeepSeek, MiniMax Distilled AI Models for Gains
    bloomberg.com · Margi Murphy, Shirin Ghaffary
  3. Anthropic accuses Chinese labs of trying to illicitly take Claude’s capabilities
    cyberscoop.com · Tim Starks
  4. Are China’s ‘AI tigers’ cheating? US rival Anthropic alleges some are
    cnn.com · John Liu

Who was involved

Alleged developer
Anthropic
Alleged harmed party
Anthropic, Claude Users, Anthropic Customers, National Security And Intelligence Stakeholders

Classification (MIT AI Risk Repository taxonomy)

Causal entity
Human
Intent
Intentional
Timing
Post-deployment
Harm level
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Sectors
—
Countries
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Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 2.2.

  • Privacy loss

    "Privacy loss - Unwarranted exposure of an individual’s private life or personal data through cyberattacks, doxxing, etc."

    A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms (Abercrombie2024)

  • Jailbreaks and Prompt Injections Threaten Security of LLMs

    "LLMs are not adversarially robust and are vulnerable to security failures such as jailbreaks and prompt-injection attacks. While a number of jailbreak attacks have been proposed in the literature, the lack of standardiz...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

  • Exploiting Limited Generalization of Safety Finetuning

    "Safety tuning is performed over a much narrower distribution compared to the pretraining distribution. This leaves the model vulnerable to attacks that exploit gaps in the generalization of the safety training, e.g. usi...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

  • “Model Psychology” Attacks

    "LLMs are vulnerable to “psychological” tricks (Li et al., 2023e; Shen et al., 2023), which can be exploited by attackers. Examples include instructing the model to behave like a specific persona (Shah et al., 2023; Andr...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

  • Attacking LLMs via Additional Modalities a

    "LLMs can now process modalities other than text, e.g. images or video frames (OpenAI, 2023c; Gemini Team, 2023). Several studies show that gradient-based attacks on multimodal models are easy and effective (Carlini et a...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

  • Vulnerability to Poisoning and Backdoors

    "The previous section explored jailbreaks and other forms of adversarial prompts as ways to elicit harmful capabilities acquired during pretraining. These methods make no assumptions about the training data. On the other...

    Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)

  • Hardware Vulnerabilities

    "The vulnerabilities of hardware systems for training and inferencing brings issues to LLM-based applications."

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Network Devices

    "The training of LLMs often relies on distributed network systems [171], [172]. During the transmission of gradients through the links between GPU server nodes, significant volumetric traffic is generated. This traffic c...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

Incidents in the same risk subdomain

All incidents in this subdomain

Source record: incident #1395 on the AI Incident Database · all 4 reports