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U.S. Agencies Accuse China AI Firms of Distilling Claude, GPT, Gemini, and Grok
NSA, CISA and FBI accuse Chinese AI firms including DeepSeek of industrial-scale distillation of Claude, GPT, Gemini and Grok since late 2024.
A joint bulletin from the NSA, CISA and FBI accuses China-based AI firms including DeepSeek, Moonshot AI, Alibaba, MiniMax, StepFun and Z.AI of systematic, industrial-scale distillation of U.S. frontier models. The agencies say billions of tokens were extracted from Claude, GPT, Gemini and Grok variants since at least late 2024 through APIs, cloud relays, obfuscated accounts and gray-market proxies, likely with Chinese government backing. Firms allegedly shared premium subscriptions across developer teams and used chain-of-thought extraction and automated failover to evade blocks. Mitigations include subtly altering responses to suspected distillers and correlating activity across providers, clouds and aggregators.
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US Agencies Warn China Is Systematically Extracting Frontier AI Capabilities
NSA, CISA and FBI warn Chinese AI firms including DeepSeek and Moonshot systematically extracted billions of tokens from US frontier models since late 2024.
The NSA, CISA, and FBI report that China-based AI companies including DeepSeek, Moonshot AI, Alibaba, MiniMax, StepFun, and Z.AI extracted billions of tokens from US frontier models such as Claude, GPT-4/GPT-5, Gemini, and Grok 4 since late 2024. The distillation trained DeepSeek's R1 and V3 and Moonshot's Kimi-K2/K3 models, and the agencies mapped the tactics to MITRE ATLAS while noting additional novel techniques like subscription exploitation and request metadata sanitization. They describe the activity as a strategic economic threat to US technological leadership and recommend behavioral detection, differential privacy, and targeted cost-imposing responses.
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Anthropic Says Seven China-Based AI Labs Ran Industrial-Scale Claude Distillation Attacks
Anthropic disrupted industrial-scale unauthorized Claude distillation by seven China-based AI labs, including Alibaba, DeepSeek, Moonshot, and Z.ai.
Anthropic identified and disrupted six illicit distillation campaigns since February 2026 run by seven China-based labs: Alibaba, Moonshot, DeepSeek, Z.ai (Zhipu), MiniMax, Xiaomi, and SenseTime. The largest, GTG-16005, involved 151 million exchanges targeting Claude Opus 4.6/4.7 chain-of-thought transcripts, peaking at roughly 3 million exchanges per day from more than 3,500 fraudulent accounts. Labs used proxy/relay services with fictitious identities, fake or stolen credit cards, harvested API keys, and purchased conversation transcripts from third-party resellers. Anthropic is countering by banning reseller accounts, summarizing internal reasoning before responding, and introducing preserved thinking in Fable 5.1, which encrypts reasoning and prevents context edits before it.
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Microsoft Offers Up to $30,000 for Critical AI Flaws in Dynamics 365 and Power Platform
Microsoft expands AI bug bounty to Dynamics 365 and Power Platform, paying up to $30,000 for critical inference manipulation flaws.
Microsoft's bug bounty program offers up to $30,000 for critical 'Inference Manipulation' or 'Inferential Information Disclosure' bugs in Dynamics 365 and Power Platform, including Copilot Studio, AI Builder, Power Apps, Power Automate, and Dataverse. Payouts scale by report quality ($30,000/$20,000/$12,000 for critical) with important-severity AI flaws earning $6,000-$20,000, plus 20% multipliers for Dataverse privilege escalation and Plugin Sandbox escapes. Prompt injection affecting only the attacker, hallucinated execution, and system-prompt disclosure are excluded from scope.
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OpenAI, Anthropic, Google API Flaw Let Weaker AI Models Decode Stronger Models' Reasoning
Researchers show encrypted reasoning blocks in OpenAI, Anthropic, and Google APIs can be replayed to recover hidden reasoning and secrets like API keys.
Researchers demonstrated that encrypted reasoning objects from OpenAI, Anthropic, and Google reasoning APIs could be replayed across sessions, users, and models, letting weaker same-family models act as decoders of hidden reasoning. Across 6,708 public agent trajectories they decoded 315,320 thinking blocks and found 704 privacy artifacts from real user sessions, including 62 API keys, 33 passwords, 24 access tokens, and seven private keys. The replayable blocks also enabled invisible prompt-injection proof-of-concepts; the main extraction attack is no longer reproducible as of August 2026 following mitigations, though no vendor has publicly acknowledged the flaw.
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Stealing AI Reasoning Traces
Researchers demonstrate a decryption jailbreak that extracts encrypted reasoning traces from Anthropic, OpenAI, and Google LLM APIs via weaker sibling models.
The paper exploits the fact that encrypted chain-of-thought blocks returned by LLM providers are interchangeable across sessions, users, and models within a provider's ecosystem. Injecting an encrypted trace into a weaker, less-safeguarded model from the same provider forces it to output the trace in plaintext, bypassing anti-distillation mechanisms. Decoding 315,320 reasoning blocks scraped from public repositories recovered 367 PII artifacts and 182 credentials, showing large-scale private data leakage. The flaw also enables hidden hazardous information disclosure and invisible prompt injections embedded in encrypted blocks; mitigations were proposed after responsible disclosure.
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Unmasking Cloud Identities: From Behavioral Clustering to Automated Detection
Unit 42 clusters behavior of 40,000+ AWS identities from 125 cloud environments to map functional roles and enable lightweight SQL-based detection.
Palo Alto Unit 42 built an unsupervised behavioral clustering model using UMAP and HDBSCAN on AWS CloudTrail logs to map cloud identities to functional roles such as administrators, backup services, security tooling and DevOps. The study analyzed over 40,000 identities across 125 cloud environments over two months. The researchers show that heuristics extracted from the clustering map can be implemented in standard SQL, enabling role classification at scale without running a continuous ML pipeline. The methodology extends to audit logs from other cloud providers, SaaS and Kubernetes.
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Hackers Got Inside a Flock Camera. Its Data Shows How the System Really Works
Hackers stole a Flock Safety camera, copied its storage, and recovered an encryption key exposing vehicle surveillance data and system internals.
Hackers from the collective stegan0gram removed a Flock Safety license plate reader from a roadway, copied its storage, and recovered an encryption key stored in an unencrypted partition, unlocking videos of thousands of vehicle detections. The data, shared with 404 Media, WIRED, and Distributed Denial of Secrets, shows the camera generated roughly 1.6 million images of about 50,200 vehicles over 21 days and detects people, bicycles, and bumper stickers in addition to license plates. The dump follows 2025 research by Jon 'GainSec' Gaines documenting flaws enabling root access, which Flock downplayed. Flock's national network makes camera records searchable by more than 2,000 agencies, fueling controversy over police and immigration lookups.
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An Evidence-First Multi-LLM Framework for Auditable Critical-Infrastructure Dependency Modeling
Evidence-first multi-LLM framework builds auditable critical-infrastructure dependency graphs while preserving provenance and unresolved cases.
The framework constructs Infrastructure Knowledge Bases and Infrastructure Dependency Graphs from heterogeneous infrastructure documentation using multiple open-weight LLMs that independently extract candidate entities and dependencies from normalized evidence. It separates evidence verification, ontology grounding, entity resolution, dependency alignment, validation, fusion, and human review, projecting the validated IKB deterministically into the IDG without new LLM-generated knowledge. Evaluation across nine infrastructure projects shows entity recovery achieves substantially higher recall than full dependency recovery, and cross-model overlap is much lower for dependencies than entities, indicating models often produce non-overlapping candidate assertions rather than stable consensus.
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LandingAI Releases Agentic Document Extraction Gen2 with DPT-3 Pro and DPT-3 Verity
LandingAI shipped Agentic Document Extraction Gen2 with DPT-3 Pro and DPT-3 Verity parsing models, adding usage-based billing, block-tree outputs, and word-level grounding.
LandingAI has generally released Agentic Document Extraction Gen2, rebuilt around two parsing models: DPT-3 Verity for deterministic transcription of digital documents with per-word bounding boxes and confidence scores, and DPT-3 Pro for layout-aware parsing of scans, handwriting, non-Latin scripts, and LaTeX math. Billing changes from a flat 3 credits per page to a page-plus-output-character model (Pro: 1 credit/page plus 0.5 credits per 1,000 output characters on priority; Verity: 0.3 plus 0.2), with an asynchronous standard tier at 0.5x price and vendor-claimed 25-80% cost reductions. Parse v2 returns a document-page-block tree with semantic IDs, normalized bounding boxes, and line- or word-level atomic grounding, replacing flat chunks; Gen1 client code will not run against Gen2 endpoints. Deployment options include US/EU cloud, VPCs on AWS, Azure, and Google Cloud, Snowflake, and air-gapped on-premises environments, with automated model routing planned for fall 2026.
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