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From Weak Cues to Real Identities: Evaluating Inference-Driven De-Anonymization in LLM Agents

ICML 2026 · Multi-agent systems and games › LLM agents · approach: Agent safety and attack benchmarks · 1 citations (OpenAlex)

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Shows LLM agents (GPT-5, Claude 4.5, o4-mini, Gemini) can reconstruct real identities from weak, individually non-identifying cues across Netflix/AOL classical linkage, a new controlled benchmark (InferLink), and redacted human-AI interaction traces, and that a privacy-aware system prompt suppresses linkage at a utility cost.

Problem

Anonymization is assumed to protect privacy once explicit identifiers are removed, but it is unclear whether LLM agents can link anonymized records to real identities via weak cues, including as a byproduct of benign tasks.

Approach

Formalizes inference-driven linkage as identity reconstruction from fragmented non-identifying cues plus auxiliary context, and evaluates it systematically by varying fingerprint type, task intent, and attacker knowledge in a controlled benchmark, alongside classical incidents and human-AI traces.

Key results

• In the sparsest Netflix regime (m=2), GPT-5 agent reconstructs 79.17% of identities versus 56.0% for the classical baseline at T=14.
• On InferLink under Explicit-MK, Claude 4.5 links at LSR 0.95-1.00 across fingerprint types, while GPT-5 ranges 0.65-0.95.
• AOL case study yields CLC=10 corroborated identities from 40 histories; Anthropic Interviewer yields CLC=6; ChatGPT logs yield CLC=1.

Surprising findings

• Claude 4.5 shows high silent linkage under implicit benign framing (LSR 0.70-0.80), while GPT-5 is more conservative.
• A privacy-aware system prompt drives LSR near zero for GPT-5 and Claude 4.5 under Explicit-MK but causes substantial over-refusal and utility loss for Claude 4.5.

Limitations

• InferLink is simplified, with a single ground-truth overlap and fixed schema of 10 records per source; harder near-duplicate regimes are not tested.
• Case studies rely on a small number of corroborable cases and cannot establish prevalence.
• Utility metric is limited to task completion and does not grade output quality.

Experimental setup

Datasets: Netflix Prize dataset AOL search logs Anthropic Interviewer dataset (Scientists subset) anonymized ChatGPT conversation logs InferLink (synthetic paired tables)

Benchmarks: Netflix Prize linkage AOL search logs InferLink Anthropic Interviewer ChatGPT logs

Metrics: linkage success rate (LSR) confirmed linkage count (CLC) utility refusal rate

Baselines: classical Netflix linkage baseline (rarity-weighted similarity) o4-mini Claude 4.5

Builds on: OpenHands Netflix Prize deanonymization (Narayanan and Shmatikov)

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