{"schema_version":"0.8.0","id":"LR-2026-0061","record_version":6,"title":"LLM pipeline deanonymizes pseudonymous Hacker News and scientist accounts","verification":{"human_verified":false,"method":"automated_gate","auto_verified":true,"score":51.7,"score_breakdown":{"source_authority":12.1,"independent_corroboration":7.0,"implicated_party_confirmed":0.0,"citation_integrity":14.0,"specificity":3.0,"completeness":8.0,"unhedged_language":5.0},"checks":{"schema_valid":true,"every_field_cited":true,"quotes_verbatim":true,"source_fetched":true,"distinct_incident":true,"dates_plausible":true,"sources_agree":true},"gate_version":"gate-2026-09-24.4","cross_check_model":null,"cross_check_agreement":null,"duplicate_of":null,"verified_by":null,"verified_at":"2026-09-25","extraction_run_id":null},"system":{"model_name":"GPT-5.2","model_version":null,"developer":"unknown","harness":null,"tools_granted":["web search"],"permissions_scope":null,"autonomy_level":"full-auto","guardrails_present":[],"deployment_context":"research","tier":null,"lifecycle_stage":"evaluation","integration_type":"agentic_with_tools","intended_use":"to figure out who anonymous users were using nothing but web search","safeguard_state":null,"production_intended":false},"event":{"date_occurred":null,"date_disclosed":"2026-09-24","incident_class":"privacy_leakage","failure_class":null,"agent_involved":true,"secondary_failure_classes":[],"trigger":"unknown","duration":null,"detection_method":"unknown","summary":"Researchers from ETH Zurich, MATS, and Anthropic built an AI pipeline that matched anonymized Hacker News accounts to real LinkedIn profiles at 90% precision.","tags":["deanonymization","privacy","llm","web-search"],"date_detected":null,"near_miss":null,"root_cause":null,"root_cause_detail":null,"failure_class_detail":"The pipeline deanonymized users by matching public posts to real identities, but the source does not describe a failure of an agent mechanism.","responsible_party":"unknown","adversarial_actor":"the_system_itself","input_provenance":"public posts from anonymous accounts","time_to_contain":null,"operations_paused":null,"remediation":"The researchers withheld their code, prompts, and every real identity they uncovered.","precursor_events":"2008 Netflix dataset deanonymization; 2025 Coinbase data breach"},"impact":{"direct_loss_usd":null,"remediation_hours":null,"records_exposed":null,"third_parties_affected_count":null,"severity":"moderate","severity_because":"moderate, because data or systems of others touched, with a bounded scope stated: The study deanonymized 338 Hacker News users, a bounded scope of third parties.","harm_class":["privacy"],"affected_description":"338 users of the mostly pseudonymous Hacker News message board","protected_class_involved":null,"data_categories_exposed":["pii"],"loss_range_usd":null,"reversibility":null,"downstream_exposure_count":null},"evidence":{"sources":[{"url":"https://decrypt.co/379228/ai-can-now-doxx-your-anonymous-accounts-heres-whats-going-on","publisher":"news search (Bing News index)","date":"2026-09-24","type":"press","title":"AI Can Now Doxx Your Anonymous Accounts? Here's What’s Going On - Decrypt","archived_url":null}],"per_field_confidence":{"system.model_name":"C","system.tools_granted":"C","system.lifecycle_stage":"D","system.integration_type":"D","system.intended_use":"C","system.production_intended":"D","system.developer":"D","system.deployment_context":"D","system.autonomy_level":"D","event.tags":"D","event.responsible_party":"D","event.adversarial_actor":"D","event.input_provenance":"C","event.remediation":"C","event.precursor_events":"C","event.agent_involved":"C","event.detection_method":"D","event.summary":"C","event.incident_class":"D","event.date_disclosed":"A","event.trigger":"D","impact.harm_class":"D","impact.affected_description":"C","impact.data_categories_exposed":"D","impact.severity":"D","context.industry":"D","context.incident_evidence":"C","context.denominator_available":"D","context.country":"D","event.failure_class_detail":"D","impact.severity_because":"D"},"field_citations":{"system.model_name":[{"source_index":0,"quote":"The attack runs on nothing but web search, embeddings, and reasoning models like GPT-5.2, at a cost of roughly $1 to $4 per target—no hacking or data breach involved."}],"system.developer":[{"source_index":0,"quote":"Researchers from ETH Zurich, MATS, and Anthropic built an AI pipeline that matched anonymized Hacker News accounts to real LinkedIn profiles at 90% precision."}],"system.tools_granted":[{"source_index":0,"quote":"In one test, the team gathered 338 users of the mostly pseudonymous Hacker News message board who had linked their LinkedIn profile in their bio, stripped out anything identifying, then asked an AI agent to figure out who they were using nothing but web search."}],"system.deployment_context":[{"source_index":0,"quote":"The researchers broke the attack into four steps they call Extract, Search, Reason, and Calibrate."}],"system.lifecycle_stage":[{"source_index":0,"quote":"In one test, the team gathered 338 users of the mostly pseudonymous Hacker News message board who had linked their LinkedIn profile in their bio, stripped out anything identifying, then asked an AI agent to figure out who they were using nothing but web search."}],"system.integration_type":[{"source_index":0,"quote":"they built a fully autonomous ai pipeline that takes your… pic.twitter.com/XMeXifhDf3"}],"system.intended_use":[{"source_index":0,"quote":"In one test, the team gathered 338 users of the mostly pseudonymous Hacker News message board who had linked their LinkedIn profile in their bio, stripped out anything identifying, then asked an AI agent to figure out who they were using nothing but web search."}],"system.production_intended":[{"source_index":0,"quote":"The researchers also didn't publish their tools, prompts, or any of the real names they uncovered, and the study went through an ethics review board before release."}],"system.autonomy_level":[{"source_index":0,"quote":"they built a fully autonomous ai pipeline that takes your… pic.twitter.com/XMeXifhDf3"}],"event.detection_method":[{"source_index":0,"quote":"The research paper making the rounds on X and Reddit again today is from a study in February, and the gist of the findings is scaring the hell out of internet anons everywhere: AI can figure out who you really are from an anonymous account."}],"event.summary":[{"source_index":0,"quote":"Researchers from ETH Zurich, MATS, and Anthropic built an AI pipeline that matched anonymized Hacker News accounts to real LinkedIn profiles at 90% precision."}],"event.tags":[{"source_index":0,"quote":"It also doesn't require a data breach, a hack, or insider access."}],"event.responsible_party":[{"source_index":0,"quote":"Researchers from ETH Zurich, MATS, and Anthropic built an AI pipeline that matched anonymized Hacker News accounts to real LinkedIn profiles at 90% precision."}],"event.adversarial_actor":[{"source_index":0,"quote":"The researchers claim that large language models, the AI systems behind Claude, ChatGPT, and Gemini, can read someone's anonymous posts and figure out who they are in real life."}],"event.input_provenance":[{"source_index":0,"quote":"AI-driven deanonymization is the same threat with the breach removed: it works off what you've already posted in public, no leak required."}],"event.remediation":[{"source_index":0,"quote":"The paper's authors withheld their code, prompts, and every real identity they uncovered, and say the study passed through ETH Zurich's ethics review board before publication."}],"event.precursor_events":[{"source_index":0,"quote":"Back in 2008, researchers cracked Netflix's supposedly anonymous movie-rating dataset by matching it against public IMDb reviews."}],"event.incident_class":[{"source_index":0,"quote":"The researchers claim that large language models, the AI systems behind Claude, ChatGPT, and Gemini, can read someone's anonymous posts and figure out who they are in real life."}],"event.agent_involved":[{"source_index":0,"quote":"they built a fully autonomous ai pipeline that takes your… pic.twitter.com/XMeXifhDf3"}],"event.failure_class_detail":[{"source_index":0,"quote":"The researchers claim that large language models, the AI systems behind Claude, ChatGPT, and Gemini, can read someone's anonymous posts and figure out who they are in real life."}],"impact.harm_class":[{"source_index":0,"quote":"The researchers claim that large language models, the AI systems behind Claude, ChatGPT, and Gemini, can read someone's anonymous posts and figure out who they are in real life."}],"impact.affected_description":[{"source_index":0,"quote":"In one test, the team gathered 338 users of the mostly pseudonymous Hacker News message board who had linked their LinkedIn profile in their bio, stripped out anything identifying, then asked an AI agent to figure out who they were using nothing but web search."}],"impact.data_categories_exposed":[{"source_index":0,"quote":"The researchers claim that large language models, the AI systems behind Claude, ChatGPT, and Gemini, can read someone's anonymous posts and figure out who they are in real life."}],"impact.severity_because":[{"source_index":0,"quote":"In one test, the team gathered 338 users of the mostly pseudonymous Hacker News message board who had linked their LinkedIn profile in their bio, stripped out anything identifying, then asked an AI agent to figure out who they were using nothing but web search."}],"context.industry":[{"source_index":0,"quote":"The researchers claim that large language models, the AI systems behind Claude, ChatGPT, and Gemini, can read someone's anonymous posts and figure out who they are in real life."}],"context.country":[{"source_index":0,"quote":"The researchers claim that large language models, the AI systems behind Claude, ChatGPT, and Gemini, can read someone's anonymous posts and figure out who they are in real life."}],"context.denominator_available":[{"source_index":0,"quote":"The researchers claim that large language models, the AI systems behind Claude, ChatGPT, and Gemini, can read someone's anonymous posts and figure out who they are in real life."}],"context.incident_evidence":[{"source_index":0,"quote":"The paper's authors withheld their code, prompts, and every real identity they uncovered, and say the study passed through ETH Zurich's ethics review board before publication."}],"impact.severity":[{"source_index":0,"quote":"In one test, the team gathered 338 users of the mostly pseudonymous Hacker News message board who had linked their LinkedIn profile in their bio, stripped out anything identifying, then asked an AI agent to figure out who they were using nothing but web search."}],"event.date_disclosed":[{"source_index":0,"quote":"published: 2026-09-24"}]},"notes_on_gaps":"Only one source (press). No source states who first noticed the deanonymization (detection_method unknown; story.caught null). No source names a failed component, so root_cause unknown and failure_class left null: the source describes a capability demonstration, not an agent mechanism failure. system.developer left 'unknown' because the cited sentence names ETH Zurich, MATS and Anthropic as builders of the pipeline, not as the trainer of GPT-5.2. autonomy_level full-auto rests on a tweet fragment calling the pipeline 'fully autonomous'. date_occurred taken from 'a study in February' (2026-02). No loss figure, no third-party count stated beyond the 338 test subjects; severity moderate on bounded scope. Country, industry, regulatory and coverage not stated. event.date_occurred: a source described it but did not state it; left null. event.trigger: unknown was classified, not stated; a trigger is never inferred, so it is unknown. event.root_cause: the stated reason names an absence, not a failed control: The paper does not name a failed component; it documents a capability of current AI models. system.tier: provider by rule (deployment_context research). [aggregated by deepseek-chat, aggregate-2026-09-27.1, 2026-10-19] system.lifecycle_stage: evaluation. system.integration_type: agentic_with_tools. system.production_intended: a test or red-team run. system.developer: no source names the developer. system.deployment_context: a test or red-team run. system.autonomy_level: classified from described behaviour. event.tags: no source states a cause. event.responsible_party: no source states a cause. event.adversarial_actor: the_system_itself. event.detection_method: no source says who first noticed. event.incident_class: personal or confidential data reached a party not entitled to it, with no attacker driving it. event.trigger: no source states a cause. impact.harm_class: privacy. impact.data_categories_exposed: pii. impact.reversibility: irreversible. context.industry: no source states a cause. context.systemic_exposure: no source states a cause. context.denominator_available: no denominator is available for this incident. context.country: no source states the operator's location. [corrected by Srini Iyer, approved 2026-10-22 in the corrections review: whose system it was did not match the record] [corrected by Srini Iyer, approved 2026-10-22 in the corrections review: a judgement inferred by us that no source states was cleared]"},"context":{"industry":"unknown","org_size_band":null,"country":"unknown","denominator_available":false,"denominator":null,"related_incident_ids":[],"systemic_exposure":null,"shared_model_id":null,"upstream_incident_id":null,"incident_evidence":{"log_completeness":null,"tamper_evidence":null,"timestamp_reliability":"unknown","investigation_independence":"embedded_evaluator","redaction_summary":"code, prompts and real identities withheld","analysis_method":"unknown","prompt_output_retention":null},"regulatory":null,"coverage":null},"narrative":{"steps":[{"step":"doing","text":"Researchers at ETH Zurich, MATS and Anthropic built a fully autonomous pipeline using web search, embeddings and reasoning models such as GPT-5.2 to identify anonymous Hacker News users from their public posts.","quote":"Researchers from ETH Zurich, MATS, and Anthropic built an AI pipeline that matched anonymized Hacker News accounts to real LinkedIn profiles at 90% precision.","source_index":0,"grade":"C"},{"step":"went_wrong","text":"The pipeline deanonymized pseudonymous users, breaching the practical obscurity that protects anonymous accounts online.","quote":"“Our results show that the practical obscurity protecting pseudonymous users online no longer holds and that threat models for online privacy need to be reconsidered,” the paper reads.","source_index":0,"grade":"C"},{"step":"reached","text":"It correctly named 226 of 338 Hacker News users and at least 9 of 125 scientists from Anthropic interview transcripts, exposing real identities.","quote":"It correctly named 226 of them, 67%, and was wrong on roughly one in 10 of the guesses it actually made.","source_index":0,"grade":"C"},{"step":"done","text":"The researchers withheld their code, prompts and every real identity uncovered, and the study passed ETH Zurich's ethics review before publication.","quote":"The paper's authors withheld their code, prompts, and every real identity they uncovered, and say the study passed through ETH Zurich's ethics review board before publication.","source_index":0,"grade":"C"}],"model":"deepseek-chat","prompt_version":"aggregate-2026-09-27.1","extracted_at":"2026-10-19","source_urls":["https://decrypt.co/379228/ai-can-now-doxx-your-anonymous-accounts-heres-whats-going-on"]}}