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AgentDoxx

AgentDoxx: Agentic Re-identification of Anonymized Text with Web Search

An evaluation suite of 822 synthetic interview transcripts grounded in public info to measure LLM re-identification risks via web search.

PaperRepo

How to run

ltb · agentdoxx
$ npm i -g lettokenburn-cli
$ ltb bench info agentdoxx

External harness. No Harbor adapter has been generated for this benchmark yet. `ltb bench info` prints the known links.

Runs happen on your machine with your own subscription; only the score summary is pushed. CLI docs

Scores

9 sourced entries
HarnessSourceConfidenceLink
Gemini 3.7 Flash97.2N/AIndependentmediumOct 4, 2026arxiv.org
GLM-5.3 Flash97.2N/AIndependentmediumOct 4, 2026arxiv.org
Kimi K397.2N/AIndependentmediumOct 4, 2026arxiv.org
Gemini 3.1 Pro96.5N/AIndependentmediumOct 4, 2026arxiv.org
DeepSeek V4 Flash90.2N/AIndependentmediumOct 4, 2026arxiv.org
GPT-5.6-terra86.0N/AIndependentmediumOct 4, 2026arxiv.org
GPT-5-mini84.6N/AIndependentmediumOct 4, 2026arxiv.org
Llama 4 Maverick42.7N/AIndependentmediumOct 4, 2026arxiv.org
GPT-4.1-mini42.0N/AIndependentmediumOct 4, 2026arxiv.org

Every number links to where it was published. Low-confidence rows are muted and need per-cell verification.

Reproduced by the community

All runs
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About

AgentDoxx comprises 822 synthetic interview transcripts derived from real individuals' public data (r/IAmA), with identifiers redacted. It evaluates agentic re-identification capabilities by isolating the impact of web search versus parametric knowledge.

Scoring relies on ground-truth identity matching accuracy. Key findings show open-weight models identify 15–28% without search, rising to >88% when targets appear in retrieved results, highlighting significant privacy risks even with entity masking.