After 24,000 Fake Accounts: How AI Model Protection Will Change

Prologue: The Next Battle Has Already Begun

Moments after Anthropic disclosed 16 million unauthorized queries, AI security researchers were already building the next line of defense. In February 2026, almost simultaneously with the Anthropic disclosure, a paper quietly appeared on arXiv. Its title: Antidistillation Fingerprinting (ADFP) — a technique that embeds statistical signatures into model outputs that survive even after a distillation attack.

When an attack is exposed to the world, defensive technology is born alongside it. Yet history shows the arms race between offense and defense never truly ends. This article projects the next phase of that race. Technology, law, regulation, industry structure — what will change across all four layers.

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1. The Four Layers of Technical Defense

The defensive architecture that AI companies are currently building is observed to consist of four layers.

Layer 1: Access Control (Fastest, Crudest Defense)

• Rate Limiting: Caps on API calls per account
• Geographic Blocking: Blocking access from specific countries or IP ranges
• Stronger Account Verification: Identity verification added during registration for academic or research accounts

Limitations: Bypassed with proxies and VPNs. Creates friction for legitimate users as well.

Layer 2: Anomaly Detection (The Method Anthropic Used This Time)

• Behavioral Fingerprinting: Detecting systematic, repetitive query patterns
• Synchronized Traffic Analysis: Identifying simultaneous similar queries across multiple accounts
• IP Correlation: Identifying hydra cluster structures

Limitations: Attackers who vary patterns irregularly can evade detection. Requires continuous updates.

Layer 3: Output Watermarking (Currently in Research and Early Deployment)

Output watermarking embeds statistical signatures, invisible to human readers, into text generated by a model. The idea is straightforward: because student models trained on watermarked outputs retain traces of the signature, those traces can be used as legal evidence.

Structural weaknesses of watermarking: However, multiple studies published in early 2026 indicate that existing watermarking algorithms can be effectively removed via distillation attacks. Two removal methods have been observed:

• Pre-removal: Diluting signatures by paraphrasing training data before distillation
• Post-removal: Neutralizing watermarks at inference time

Layer 4: Antidistillation Fingerprinting (Latest Research Stage)

ADFP (Antidistillation Fingerprinting) (Xu et al., 2026, arxiv 2602.03812) emerged to overcome the weaknesses of conventional watermarking. The key differences are as follows:

Conventional Watermarking	ADFP
Signature can be lost during distillation	Signature insertion tuned to the student model's learning dynamics
Relies on arbitrary biases	Token selection optimized to maximize post-distillation detectability
Can be removed by attack	Statistical signature survives through the distillation process

ADFP uses a proxy model to determine in advance which tokens best transfer the signature to the student model, then concentrates signature insertion on those tokens. No large-scale commercial deployment has been confirmed yet, but this approach is expected to become the technical foundation for AI model IP protection going forward.

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2. The Watermarking War: The Next Phase of the Arms Race

The pattern that the history of technical defense reveals is clear. Once a defensive technology is published, attackers develop circumvention methods to match. Now that ADFP has been made public, research on bypassing ADFP may already be underway.

[Current State]
Watermarking introduced → Watermark removal research → ADFP → ADFP bypass research → ...

[Structural Conclusion]
Technical defense alone cannot achieve complete blockage
→ Legal and regulatory supplements are essential

The one point where the defense side holds an advantage in this arms race is making the cost of attacks high enough to undermine the economic rationale for distillation. Complete blockage is not the realistic goal — cost structure reversal is.

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3. The Reality of Legal Protection: Why Copyright Doesn't Work

The Limits of Copyright

The common conclusion from leading law firms including Fenwick and Winston & Strawn is:

"Under the current legal framework, copyright is unlikely to provide substantial protection against model distillation."

Three reasons:

1. Unsettled copyright status of AI outputs: Whether AI-generated text qualifies for copyright protection has not yet been established by the courts
2. Idea-expression dichotomy: Copyright protects expression, not ideas, style, or reasoning methods. Distillation attacks absorb capabilities and reasoning methods, not specific expressions
3. Fair use uncertainty: Whether using data for research or training purposes qualifies as fair use is currently being contested in multiple lawsuits

Three Realistic Legal Pathways

① ToS Violation + Civil Damages

The most realistic pathway. A breach of contract claim can be made on the basis of terms of service violations. However, proving damages is difficult and jurisdictional issues remain.

② Patents (Promising for the Medium-to-Long Term)

Fenwick analyzes that patents can offer protection not only for teacher models but also for student models created through distillation. Filing patents on model architecture, training methods, and specific inference mechanisms — combined with technologies like ADFP — can become a means of legally proving distillation origins. The time limitation, however, is that patent filing and registration takes years.

③ Trade Secrets (Immediately Applicable)

US federal and state law protects trade secrets when reasonable protective measures have been taken. Measures such as API key management, purpose-of-use agreements, and enhanced access controls can satisfy the "reasonable measures" requirement for trade secret protection. Immediately applicable, with the additional possibility of cross-claims with export control violations.

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4. Regulatory Pathways: Directions in the US, UK, and EU

United States: Innovation-First, but Chip Controls Tightening

The Trump administration's second term is reluctant to pursue comprehensive AI regulation legislation under an "innovation-first" stance. However, AI chip export controls enjoy bipartisan support. The shared lobbying effort by Anthropic and OpenAI is likely targeting exactly this point.

Realistic US regulatory pathways:

• Strengthening AI chip export controls (HB/SB discussions ongoing)
• Sanctions on unfair AI practices under the Trade Act
• Comprehensive AI legislation unlikely to pass in the short term

United Kingdom: The Most Concrete Timeline

The UK government is expected to release two official reports on AI training data use and copyright protection by March 18, 2026 (based on the Data Use and Access Act 2025). The core agenda includes the balance between AI developers' rights and copyright holders' rights, and frameworks for protecting AI-generated outputs under UK law.

These reports could become the first instance of a government formally articulating legal protection standards for AI model outputs — potentially establishing a global reference benchmark.

EU: Intersections with the AI Act

The EU AI Act has been entering into force in phases since 2025. Transparency and data governance requirements for high-risk AI systems are indirectly connected to distillation attack prevention. However, the EU AI Act itself does not directly regulate distillation attacks. There is potential for this agenda to be included in future amendment discussions.

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5. Industry Self-Regulation: Possibilities and Limits

The Rise of Information-Sharing Consortiums

Anthropic's mention of "sharing information with industry partners" as one of its detection methods is a meaningful signal. The formation of an AI security intelligence consortium — in which major AI companies like OpenAI, Anthropic, and Google share attack patterns — is a possibility worth watching.

Precedent: FS-ISAC (Financial Services Information Sharing and Analysis Center) in finance, CISA information-sharing frameworks in cybersecurity. If a similar structure emerges in the AI industry, early-detection capability for distillation attacks could exceed what any individual company can achieve.

Structural Limits of Self-Regulation

However, information sharing between competitors carries a fundamental incentive conflict. Sharing attack patterns could indirectly expose information about a competitor's model security posture. Sustaining this cooperation through self-regulation alone, without a regulatory foundation, will be difficult.

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6. Outlook: 3–5 Year Scenarios

Scenario 1: Simultaneous Technical and Regulatory Tightening — "Cost Reversal" Achieved (Probability: 55%)

If ADFP and other advanced technical defenses, strengthened chip export controls, and legal standard-setting in the US and UK all materialize together, a "cost reversal" could occur — where the cost of distillation attacks approaches the cost of training from scratch. In this scenario, unauthorized distillation decreases but does not disappear entirely.

Scenario 2: Technical Arms Race Continues, Regulation Lags (Probability: 75%)

The most realistically likely path. Technical offense and defense compete, while law and regulation fail to keep pace with technology. In this case, AI companies continue sustained defensive investment targeting "cost management" rather than "complete blockage." API terms of use grow more complex, and access to high-value capabilities becomes progressively stricter.

Scenario 3: China AI Completes an Independent Ecosystem, Reducing Attack Incentives (Probability: 40%)

If access to US AI systems becomes persistently difficult, Chinese AI companies may increasingly focus on developing frontier model capabilities independently. In this case, the rationality of distillation attacks may itself diminish within 3–5 years. This scenario assumes, however, that China's independent training capabilities advance substantially beyond their current level.

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7. Practical Decision-Making Guide

For Companies Developing or Operating AI Models and Services

Check Question	If Yes — Priority Action
Have you evaluated deploying output watermarking?	Assess feasibility of ADFP and similar fingerprinting technologies
Is there a patent strategy for model-related IP?	Begin evaluating patentability of AI architecture and inference methods
Are API purpose-of-use agreements explicitly in place?	Legal review of trade secret protection requirement compliance
Is there an industry AI security information-sharing channel?	Join a consortium or build an internal intelligence channel

For Companies Adopting or Using AI

Check Question	If Yes — Priority Action
Are you tracking ToS changes from AI service providers?	Automate monitoring of major AI vendor ToS updates
Has stronger AI model protection regulation been factored into procurement plans?	Evaluate parallel strategies for open-source and fine-tuned alternatives
Is there a plan to review the UK AI copyright report (expected 2026-03-18)?	Block time for legal team review immediately after publication

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8. What Not to Overestimate

Risk 1: Expecting "ADFP Will End Distillation Attacks"

ADFP is a promising technology, but research on watermark removal is already active. The more new defensive technologies are published, the faster bypass research accelerates. ADFP is an important tool for raising attack costs, but it is difficult to view it as a final solution.

Risk 2: Optimism That "Regulation Will Be Established Quickly"

Given the US "innovation-first" stance and legislative pace, comprehensive AI model protection legislation is unlikely to be established within 2–3 years. The UK report (2026-03) may be an important milestone, but additional time will be needed before it translates into legislation.

Risk 3: Treating "This Problem as China-Specific"

The technical methods of distillation attacks are not nationality-specific. This incident was carried out by Chinese companies, but as long as APIs remain open, any country or actor can attempt similar attacks. Protection technologies and policies must be designed as structural responses, not measures targeting a specific country.

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Epilogue: Finding a New Balance Between Openness and Protection

The AI industry now faces an unfamiliar question: how do we protect the capabilities of a model? Technology alone, law alone, regulation alone — none provides a complete answer.

One thing is certain: this question has now become the entire industry's agenda. Distillation attacks existed before Anthropic's disclosure. The difference is that they have now become an open war. An open war accelerates investment in defensive technology, the establishment of legal standards, and regulatory debate.

The AI ecosystem 3–5 years from now will look different from today. API access will become more conditional. Legal traceability signatures will be embedded in model outputs. AI model protection will be managed through a composite framework of patents, trade secrets, and contracts. The speed and direction of that change is being decided at this very moment.

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Key Action Summary

Role	Check Immediately	Check Within 6–12 Months
AI Model Development Team	Evaluate output watermarking and ADFP technologies	Develop patent strategy for core model capabilities
Legal / IP Team	Verify compliance with trade secret protection requirements for AI models	Review patent portfolio and ToS enforceability
AI Policy Lead	Confirm UK AI copyright report publication date (2026-03-18)	Monitor US chip export controls + EU AI Act amendment developments
Security Team	Re-examine anomaly detection logic and account verification rigor	Evaluate participation in AI security intelligence-sharing consortiums

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Frequently Asked Questions

Q1. Is ADFP already commercially deployed?

No commercial deployment has been confirmed as of yet. It is a research-stage technology published on arXiv in February 2026. However, the timing of the ADFP paper overlapping with Anthropic's statement that it is "developing model-level protections" suggests that active development in this direction is underway across the industry.

Q2. Does output watermarking degrade user experience?

Well-designed watermarking is engineered to have no noticeable impact on output quality. However, some methods introduce weak biases in certain token selections, so it would be inaccurate to say the quality-security trade-off is entirely zero. ADFP was explicitly designed to minimize this trade-off.

Q3. Why does the UK report (2026-03-18) matter?

Because it could be the first time a government officially articulates legal protection standards for AI model outputs. The UK's direction has the potential to serve as a precedent that influences discussions in the EU and US. In particular, if the report takes a clear position on whether AI-generated outputs qualify for copyright protection, it could directly affect the IP strategies of global AI companies.

Q4. Do open-source model companies benefit from these changes?

There are relative short-term benefits. As restrictions on access to closed-API models tighten, the relative appeal of open-source models like Llama and Mistral increases. However, in the medium-to-long term, the possibility that open-source models become part of security regulation discussions cannot be ruled out. US government discussions may expand toward separately regulating the overseas spread of open-source models.

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Related terms / next reading

• Model Distillation
• Output Watermarking
• Behavioral Fingerprinting
• Antidistillation Fingerprinting (ADFP)

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Related Reading

• Part 1: 16 Million Queries — How China's AI Trio Used Claude as a Textbook
• Part 2: The Distillation War — How Anthropic's Disclosure Revealed the Structural Anatomy of US-China AI Theft
• Open Source vs Closed AI Stack: What Should You Choose?
• Enterprise AI Governance: Pre-Adoption Checklist

Series Complete

• Part 1 (2026-02-25): Method & technique — how it was done
• Part 2 (2026-02-26): Structure & competition — why it was possible
• Part 3 (current): Regulation & future — how AI model protection will change

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Update Notes

• Content reference date: 2026-02-27 (KST)
• Update trigger: UK report release (2026-03-18) and major legislative developments
• Next scheduled review: 2026-04-01

Reference Links

• Antidistillation Fingerprinting (arXiv): https://arxiv.org/abs/2602.03812
• Fenwick AI IP Analysis: https://www.fenwick.com/insights/publications/deepseek-model-distillation-and-the-future-of-ai-ip-protection
• Winston & Strawn Legal Analysis: https://www.winston.com/en/insights-news/is-ai-distillation-by-deepseek-ip-theft
• Anthropic Official Statement: https://www.anthropic.com/news/detecting-and-preventing-distillation-attacks
• Slaughter and May AI 2026 Outlook: https://www.slaughterandmay.com/horizon-scanning/2026/digital/ai-update-for-2026/