The U.S. AI Technology Speech Hijacking Incident Involving 16 Million Conversations: A Comprehensive National Security Alert

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Massive Technology Theft Shakes U.S. AI Industry: Anthropic Reveals Detection and Response to Illegal Claude Distillation Attacks by Chinese AI Firms

How did Chinese AI companies illegally extract the core capabilities of America’s advanced AI models through over 16 million confidential conversations? This incident goes far beyond simply “catching up with competitors’ technology”; it’s a sophisticated attack combining large-scale automation and organized evasion infrastructure that effectively turned commercial models into ‘learning data factories.’

Illegal Distillation Scheme Turning 16 Million Conversations into ‘Learning Material’

Anthropic disclosed that three Chinese AI firms used distillation techniques to massively collect Claude’s responses and train their own models to imitate its behavior. Distillation, originally a legitimate method that uses a large model’s (teacher’s) outputs to efficiently train a smaller model (student), was transformed here into an illegal model extraction technique by combining terms-of-service violations, access restriction bypasses, and large-scale fake account operation.

This attack was not a mere scraping of a few answers. Over 24,000 fake accounts were deployed, resulting in more than 16 million conversations. Rather than a “human testing” approach, it resembled a programmed data collection pipeline designed to extract targeted functionalities as massively as possible.

Diverse Theft Targets by Each Company: Precise Collection from Logic, Coding to Tool Use

A fascinating aspect is how the three firms optimized their collection targets differently despite launching the same attack. Instead of collecting indiscriminately, they strategically gathered Claude’s strongest—and competition-critical—capabilities.

• DeepSeek focused on over 150,000 conversations targeting basic logic, alignment, and evasion patterns in policy-sensitive queries, effectively studying how the model’s “safety mechanisms operate.”
• MiniMax amassed the bulk of data—13 million conversations—zeroing in on agentic coding, tool use, and orchestration skills. They even optimized operations by quickly switching traffic to new models after updates to avoid missing fresh data.
• Moonshot AI collected over 3.4 million conversations broadly covering agentic reasoning, coding/data analysis, and computer vision, mixing multiple access routes to mask organized activity.

Because the targets were not just “correct answers” but the product’s competitive core—advanced reasoning, tool integration, and task automation—this case is viewed less as account abuse and more as industrial espionage-level technology theft.

Evasion Infrastructure at the Core: ‘Hydra Cluster’ Proxies Defy Access Restrictions

A bigger question looms: how did attackers continuously operate so many accounts? According to Anthropic, attackers built a proxy service known as a hydra cluster to run tens of thousands of accounts simultaneously inside China’s access-restricted environment. One proxy network reportedly operated over 20,000 fake accounts concurrently, highlighting the sheer scale.

Technically, this proxy-based operation aimed to:

1) Circumvent regional restrictions and blocks, enabling service access;
2) Mix traffic with normal users to dilute anomalies and evade detection.

In essence, the attack’s core was not “asking many questions to Claude,” but designing a large-scale automated collection system predicated on detection evasion.

Anthropic’s Disclosed Detection and Response: Behavioral Fingerprints and Chain-of-Thought Pattern Classification

One reason this issue shook the U.S. AI industry is Anthropic’s transparency—not just issuing a ban notice, but revealing technical clues on how they caught the attack.

Anthropic highlighted the following approaches:

• Behavioral fingerprinting: Tracking behavioral features unlikely from human users—account creation, usage patterns, request frequency, session structures—to identify clusters of suspicious accounts.
• Chain-of-thought distillation pattern classifiers: Detecting distillation signals via classifiers analyzing repeated query types, prompts encouraging stepwise reasoning, and attempts to overly extract model’s internal deliberation.
• Strengthened authentication and account issuance: Securing vulnerable channels such as educational or startup accounts to break the chain from “mass account issuance → automated data collection.”
• Industry-wide sharing: Collaborating with other AI firms, cloud providers, and authorities to nullify attack infrastructure at the ecosystem level.

Ultimately, the clear message left by this incident is this: in the frontline AI model race, security is no longer a mere add-on—it’s a core product capability protecting the model itself. Because defending against such threats can’t be done solo, the industry is now entering a phase of sharing a common threat model together.

The Sophisticated Mechanics of Distillation Attacks: Technical Insights Revealed by Anthropic’s Disclosure of Illegal Claude Distillation Attacks by Chinese AI Companies and Their Countermeasures

What secrets lie behind an attack systematically conducted using tens of thousands of fake accounts and proxy networks? This incident goes far beyond simply “creating many accounts.” It is closer to an automated model extraction pipeline engineered to simultaneously bypass access restrictions, detection, and rate limits.

The Architecture Enabling Large-Scale Distillation: The “Account-Proxy-Automation” Triangular Formation

Distillation originally is a legitimate technique where a smaller model (student) learns from the outputs of a larger model (teacher). However, performing it without authorization constitutes “illegal model extraction.” The key is not just a few queries, but reliably harvesting enough (query, response) pairs to serve as training data. To achieve this, attackers typically combine these three elements:

• Fake Accounts (Identity Layer): To distribute usage limits, policy enforcement, and regional restrictions at the account level
• Proxy/Relay Network (Network Layer): To evade IP-based blocks, country-level access restrictions, and anomaly detection
• Automation Orchestration (Automation Layer): For mass prompt generation, result collection and refinement, retry on failure, and coverage of various model capabilities (tool usage, coding, reasoning)

According to Anthropic’s recent revelations, Chinese AI companies scaled this architecture to the extreme by operating tens of thousands of accounts concurrently via “hydra cluster” proxy services, interleaving with regular user traffic to complicate detection. In other words, their approach disperses network, account, and request patterns all at once.

The Role of Proxy Networks: Beyond Regional Blocks to “Detection Evasion”

In environments with restricted Claude access, a simple VPN is insufficient. Instead, a proxy assembly becomes necessary, performing two critical roles:

1. Diversifying Access Paths
   Making traffic appear to originate from diverse points to evade country/ASN/IP range blocks.

2. Blending Behavioral Footprints
   Avoiding sudden traffic spikes “all in one place” by spreading queries across nodes, creating the effect of gradual ramp-up. Especially when mixed with normal user patterns, simple threshold-based monitoring is easily neutralized.

This strategy favors long-term, smooth data collection rather than “large bursts at once.” Distillation attacks are alarming because they do not instantly damage models but gradually replicate a model’s capabilities over time.

What and How They Extracted: Function-Specific Prompt Design and Systematic Coverage

For large-scale extraction to succeed, queries cannot be random—they severely reduce learning efficiency. Piecing together the disclosed information, attackers likely designed prompts by functional units such as:

• Policy-Sensitive Areas: Repeatedly probing boundary conditions that trigger safety mechanisms (e.g., obfuscated expressions, stepwise questioning, roleplay)
• Reasoning/Alignment Characteristics: Collecting extensive data on logic progression, refusal phrase patterns, and safe alternative suggestion styles
• Agentic Coding and Tool Usage: Maximizing loops of “planning → code generation → execution/verification → revision”
  (This produces chained interactive data with higher training value than single responses.)

Thus, rather than merely copying Q&A pairs, the focus was on replicating the ‘procedures’ of interactions the model performs best. Especially, tool use and orchestration capabilities represent cutting-edge advantages in modern models, making them prime targets for attackers.

Post-Processing Transforming Massive Requests into “Training Data”: Refinement, Labeling, and Filtering

The final puzzle is converting approximately 16 million collected conversations into trainable datasets. Typically, this involves:

• Deduplication: Compressing identical or similar variants to enhance data quality
• Quality Filtering: Categorizing short, meaningless, erroneous, or policy rejection responses based on purpose
• Difficulty/Domain Tagging: Labeling by code, math, reasoning, safety policy, etc., to structure a training curriculum
• Multi-turn Dialogue Reconstruction: Preserving sessions where process matters, such as agentic interactions

Ultimately, attackers did not just steal “conversation logs,” but mass-produced raw materials for model training factories.

Why Traditional Security Measures Fall Short: The Limits of Account-Based Defenses

Many services respond with account suspensions, IP blocks, and rate limiting. But when tens of thousands of accounts are combined with proxy networks, defense becomes drastically harder:

• Anomalies in one account represent only a fraction of total attack traffic
• Blocking IPs is futile as origins keep changing
• Lowering request rates still allows attackers to meet targets by extending duration

Hence Anthropic stresses the importance of behavioral fingerprinting and chain-of-thought extraction pattern detection—a “pattern-centric defense.” Detecting large-scale distillation requires catching traces left by the intent and structure of requests, not just account or network clues.

This disclosure of Anthropic’s detection and response to illegal Claude distillation by Chinese AI companies reveals how industrialized these attacks have become—and highlights how defenses must evolve beyond mere blocking toward analyzing behavior, sequences, and intent.

Anthropic Reveals Detection and Response to Illegal Distillation Attacks by Chinese AI Firms on Claude: Cutting-Edge Detection Technologies and Response Strategies

Complex attacks often cannot be caught by simple indicators like “increased traffic.” What makes this case particularly impressive is that Anthropic captured sophisticated patterns disguised as tens of thousands of fake accounts and proxy networks through the very “behavioral” traces they left behind. In other words, they tracked how the system was used rather than just who accessed it, effectively stopping large-scale extraction.

Behavioral Fingerprinting: Identifying ‘Usage Habits’ Instead of Accounts

Conventional misuse detection relies on static signals such as IP addresses, payment methods, and account creation information. But when traffic is dispersed via proxy services like “hydra clusters,” static signals are quickly neutralized. Anthropic emphasizes the solution as behavioral fingerprinting.

Behavioral fingerprinting combines the following dynamic patterns to distinguish between “normal user groups” and “automated model extraction groups”:

• Abnormal conversation flow: Unlike natural human dialogue, distillation-oriented queries tend to exhibit consistent templates, excessive repetition, and a skew toward specific tasks (coding/tool use).
• Query-response consumption patterns: When gathering results as model training data, session switching, request intervals, and response length preferences tend to be fixed—unlike typical exploratory user behavior.
• Signs of large-scale parallel execution: Even with many accounts, if orchestration methods (task distribution structures) are similar, request patterns statistically converge. This similarity becomes a “fingerprint.”
• Systematic scanning of policy-sensitive queries: Aggressive probing of specific categories of questions (such as censorship circumvention or policy boundary testing) often separates these behaviors from normal product use.

The key is not “a single suspicious account” but detecting cluster patterns formed by multiple accounts together. Thus, the more proxies and fake accounts involved, the easier it is for attackers to leave identical automated traces.

Chain-of-Thought (CoT) Extraction Pattern Detection: Targeting the Moment They Steal ‘Trainable’ Data

Anthropic also highlights a dedicated chain-of-thought extraction pattern detection classifier. Distillation attacks are not just about collecting answers but aim to gather the model’s reasoning style, alignment traits, and boundary responses in forms reproducible as training data. Attack traffic often exhibits characteristics such as:

• Repeated prompts that induce reasoning processes (e.g., explanations, stepwise logic, forced justification)
• Rephrasing the same problem in multiple variants to map boundary conditions
• Requests scanning the “response surface” of alignment/safety policies (related to weaponization, illegal acts, censorship evasion)

While individual requests may appear normal, combined with volume, repetition, and variation, they reveal designs for “extraction-focused experimental setups.” The classifier probabilistically identifies this intent and triggers blocks.

Strengthening Account Creation Paths: Closing the ‘Easy Gateways’ Exploited by Attackers

Detection alone isn’t enough; if attackers keep creating accounts, it becomes a war of attrition. Anthropic disclosed strengthening authentication and onboarding pathways prone to abuse by fake accounts, such as educational or startup accounts. Reinforcements at this stage typically include:

• Stepwise tightening of identity, payment, and organization verification (risk-based)
• Early throttling on mass sign-ups and token consumption
• Chained analysis of suspicious account clusters (blocking one exposes related accounts)

This strategy raises the cost from the registration phase and suppresses abuse collectively at the operational phase, aiming to break the loop of “Detect → Block → Re-signup.”

Industry Collaboration and Joint Defense: Why This Is Not a ‘Fight for One Company’

Anthropic repeatedly emphasizes that such attacks cannot be contained by any single company’s defenses. Due to intertwined proxy infrastructure, cloud resources, and account creation channels, defense expands through:

• Sharing detection signals and attack tactics (TTPs): Information sharing among AI firms and cloud providers rapidly exhausts attackers’ reusable infrastructure.
• Cooperation with authorities: Large-scale automated account management and regional access evasion intersect with policy and enforcement beyond pure technical issues.
• Elevating defense at the model provisioning stage: Beyond simple rate limits, evolving toward behavioral and intent-based detection distinguishes “normal use” from “extraction-focused use.”

In summary, Anthropic’s response is not just about “blocking traffic” but an example of dismantling the attacker’s operational system (accounts, proxies, automation, extraction goals) through behavioral fingerprints. This approach is highly likely to set a standard defense strategy that spreads across frontline AI services going forward.

Anthropic Reveals Detection and Response to Illegal Distillation Attacks by Chinese AI Firms on Claude: Implications for AI Export Controls and National Security – The Gravity of the Threat

What potential risks do hijacked AI models pose to national security? The core issue isn’t “performance” per se, but the ripple effect caused when frontline-level capabilities stripped of safety measures slip beyond control. This is precisely what Anthropic highlighted by disclosing the detection and mitigation of illegal distillation attacks on Claude by Chinese AI companies. It is a warning that this is not merely a breach of terms of service but an incident capable of undermining the very purpose of export controls—that is, suppressing the proliferation of hazardous capabilities.

Why ‘Distilled Models’ Without Safety Measures Represent a Dangerous Technical Threat

Distillation is a method where a large model (the teacher) generates voluminous outputs to train a smaller model (the student). The problem is that this process doesn’t just transfer knowledge; it can also “replicate” the following:

• Transfer of Dangerous Task Capabilities: Even if the model doesn’t directly produce harmful content, accumulated procedural explanations, alternative pathways, or tool usage knowledge—“actionable” data—pose significant risks.
• Loss of Alignment: If the distilled model learns to bypass original model safeguards (e.g., prohibited synthesis routes, attack procedures) during the distillation, the student model may lack the same suppression mechanisms.
• Unrestricted Distribution in Operational Environments: API-based services allow monitoring, rate limiting, and policy enforcement, but once a distilled model is deployed on private servers or on-premises, tracking and blocking harmful behavior becomes nearly impossible.

This is why Anthropic warns that “illegal distillation can lead to the spread of models stripped of safety mechanisms.”

The Undermining of Biochemical Weapons Development Prevention Mechanisms

Modern AI safety policies combine tools that block direct knowledge transfer, implement staged refusals to high-risk requests, and offer warnings and mitigation guidance in biochemical fields. However, if distilled models weaken these safety measures, risks may escalate via:

1. Automation of Experiment Design: While not synthesizing harmful substances directly, knowledge on “failure conditions” or “alternative reagents” can be deadly if combined and applied.
2. Fragmented Information Leakage: A safe model might block entire procedures, but a looser model might allow accumulation of fragmented knowledge through repeated queries. Attackers could then reassemble this into effective guides.
3. Realization Through Tool Integration: Models connected to web search, databases, and code execution tools can generate concrete operational plans, and when combined with agentic capabilities, risk levels soar dramatically.

In essence, the key question is not “how smart the model is” but “where and how risky queries are blocked.” Distillation blurs this crucial line of control.

Why Cyberattack Prevention Is at Risk: The Challenge of Agentic Coding and Scale

What stands out in this incident is the focus on extracting not just simple Q&A data but agentic coding, orchestration, and tool usage capabilities. The uncontrolled spread of such powers in cyber domains can trigger:

• Automated Attack Chain Generation: Steps such as vulnerability discovery → PoC creation → privilege escalation → lateral movement → evidence elimination may be rapidly orchestrated by the model itself.
• Sophistication of Phishing and Social Engineering: Not mere sentence crafting, but scenario design tailored to target organizations and business contexts, easing detection evasion.
• Worsening Asymmetry in Defense: Defenders must block all paths, whereas attackers need only one success. Even a slight increase in attack success rates driven by models can exponentially amplify societal harm.

Ultimately, this is why AI export control discussions pivot from mere “technological rivalry” to managing the proliferation of automated attack capabilities.

Why ‘Export Control’ Is Becoming a Core Issue Again

Anthropic’s challenge can be summarized as follows: The mere feasibility of illegal distillation strengthens the policy rationale for controlling frontline AI proliferation. Especially since large-scale distillation requires not only data collection infrastructure but also significant compute resources, naturally linking control discussions to access to advanced semiconductors and cloud computing.

However, it is crucial to recognize that “strengthened controls” don’t automatically guarantee “safety.” Because of workarounds like proxy networks, mass fake accounts, and blending with legitimate traffic—as seen here—effective policy must combine technical countermeasures such as behavior-based detection, account creation verification, and chain-of-thought extraction pattern classification for real-world efficacy.

In short, the case Anthropic revealed demonstrates the reality that once AI becomes a national security asset, model security and export control must be treated as an integrated set. If the risk shifts unchecked from “services with safety mechanisms” to “replicated models without safety mechanisms,” containment will collapse across both biochemical and cyber domains.

Joint Response for the Future: Industry and Policy Challenges Raised by Anthropic’s Disclosure of Chinese AI Firms’ Illegal Claude Distillation Attacks Detection and Mitigation

From strengthened semiconductor export controls to global cooperation, the reasons why AI industry players and policymakers must unite are clear. Once a model is “distilled,” simply blocking accounts is no longer enough to stop its spread. The core message Anthropic conveyed through its public disclosure of detection and response to illegal distillation attacks on Claude by Chinese AI companies is that “technological defense alone is insufficient; policy and industrial frameworks must move in tandem.”

Technology: The Focus of Defense Shifts from ‘Accounts’ to ‘Behavior’

With the rise of massive fake accounts and proxy networks (hydra clusters), traditional security measures—like strengthened account authentication, IP blocking, and simple rate limiting—are easily bypassed. Thus, the defense focus pivots to behavior-based detection, as illustrated below:

• Behavioral Fingerprinting: Unlike regular users, illegal distillation generates large volumes of repetitive and uniform query patterns. For example, sending thousands to tens of thousands of slightly altered similar prompts in a short time, or scraping specific capabilities (such as alignment bypass, tool usage, or code generation) intensively results in traffic that stands out statistically.
• Chain-of-Thought Extraction Pattern Detection: Attempts to replicate not just response quality but the “model’s internal reasoning style” leave traces in query structure. Detectors combine prompt length, stepwise requisites, repetitive patterns, and signals of sensitive policy evasion to classify correlated patterns indicative of extraction purposes.
• Collaborative Defense at Cloud and API Levels: Attackers survive by rotating proxies and account pools. Hence, relying on logs from a single model provider falls short; signal sharing among cloud providers, CDNs, payment processors, and authentication services becomes decisive for detection accuracy.

The technological takeaway is that “AI security has evolved beyond protecting the model itself to observing and correlating the entire API supply chain.”

Policy: Export Controls Target Not Only ‘Chips’ but Also ‘Training Capabilities’

This incident ties into national security because successful illegal distillation means high-performance models with weakened safeguards can proliferate widely. Two intersecting policy points stand out:

1. Redefining Semiconductor Export Controls
   Anthropic’s argument sums up as: “Executing distillation at this scale ultimately requires massive computation, linking access to advanced semiconductors.” Thus, chip controls evolve from simple supply chain sanctions to tools that cap the capability of large-scale model replication and distillation itself.

2. International Standardization of Model Access Controls Needed
   Geofencing alone is penetrable by proxy networks. Therefore, policies must evolve to institutionalize access via:

   • Enhanced KYC and account verification for high-risk users and organizations,
   • Purpose-based access policies for high-performance model APIs,
   • Auditing and reporting frameworks for large-scale automated calls.

The policy essence is not “giving up because it’s unpreventable,” but structurally raising the cost of large-scale illegal distillation to create effective deterrence.

Industry: Why ‘Every Company for Itself’ Is Impossible—Information Sharing Becomes a Competitive Advantage

As Anthropic stressed, no single company can solve this alone. Attackers access multiple services simultaneously and shift easily if blocked. Therefore, industry-wide cooperation isn’t optional—it’s essential.

• Sharing Attack Intelligence: Proxy indicators, account creation patterns, automated tool signals, and abnormal call graphs must be exchanged in common formats. Proven effective in spam and phishing defense, the same network effect applies to AI APIs.
• Standardized Rate Policies and Distillation Prevention Designs: Individual company policies become “research targets” for evasion. By contrast, industry-wide defense standards raise the difficulty of circumvention.
• Shifting ‘Safety Features’ from Competitive Edge to Infrastructure: Given that the removal of safeguards can escalate to national security risks, safety-related technologies (policy compliance, sensitive domain protection, abuse detection) should be treated as shared industry infrastructure.

Ultimately, this case demonstrates that “technological competition” and “security risk” now run on the same track. Illegal distillation is not merely a theft of models but an attack that undermines AI governance itself. Hence, semiconductor export control, access policies, and industry cooperation are not separate agendas—they must be designed together as an integrated package to safeguard the future.