A new study suggests anonymous accounts may not be as anonymous as we once believed.
If you have ever experienced stalking, trolling, or online abuse, this episode is for you.
The research shows how large language models can be used to perform deanonymisation at scale. As the paper puts it:
“The practical obscurity protecting pseudonymous users online no longer holds.”
Today’s episode is one of those moments where you realise just how powerful this technology has become. Personally, I’m grateful to be living in the AI age. As someone who has experienced online stalking, I find the idea that AI could help expose dangerous trolls incredibly encouraging.
If this technology works as researchers suggest, it could do two important things:
- Help bring perpetrators to justice
- Make people think twice before posting abusive or threatening content online
The Research
The paper, titled “Large-scale online deanonymization with LLMs,” describes a system that can strip away digital anonymity at an industrial scale.
The researchers demonstrate how modern AI agents can:
- analyse what someone posts online
- search the wider web for clues
- link pseudonymous accounts to real-world identities
And they can do this far more efficiently than a human investigator.
The system was tested on:
- nearly 1,000 Hacker News accounts that link to real LinkedIn profiles
- around 10,000 Reddit users
The only input used was their public posts.
The cost of the experiments was surprisingly low. The total came in at under $2,000, with each profile costing between $1 and $4 to analyse using standard APIs.
What once required skilled investigators and many hours of work can now be automated and run at scale. The cost barrier that once protected ordinary users has largely disappeared.
The Double-Edged Sword
Of course, this raises serious concerns.
The same tools that could help expose trolls and abusers could also be used against whistleblowers, journalists, activists, and dissidents who rely on pseudonyms for safety.
But it does suggest something else too: the era of completely consequence-free anonymous trolling may be coming to an end.
And if you are an employee running a Reddit alt, a secret X account, a finsta, or a Glassdoor profile where you vent about your boss, the assumption that anonymity will protect you is much shakier than it was even a few years ago.
Speaking about the research, Daniel Paleka, a researcher at ETH Zurich, told The Verge:
“Information on the internet is there forever.”
That permanence could create real-world risks for journalists and activists who rely on anonymity. It could also enable things like hyper-targeted advertising and highly personalised scams.
The paper’s broader conclusion is stark:
Pseudonymity on its own no longer provides meaningful protection online.
The Experiments
Experiment 1: Agentic Attacks on the Open Web
Researchers built an AI agent equipped with web search tools. They gave it anonymised summaries of 338 Hacker News users who had originally linked to their LinkedIn profiles.
Direct identifiers such as names, URLs, and handles were removed. What remained were only posts and bios — the type of information a human investigator could theoretically work from.
The results were striking.
- 226 out of 338 users were correctly re-identified
- That represents 67% recall at 90% precision
- There were 25 incorrect identifications and 86 abstentions
Each profile analysis costs between $1 and $4, keeping the total cost of the experiments under $2,000.
The researchers also tested the system on Anthropic’s Interviewer dataset, which includes 33 anonymised interviews with scientists discussing how they use AI.
Using only the transcripts and web search, the AI correctly re-identified 9 out of 33 participants at 82% precision.
Experiment 2: The ESRC Pipeline
The second set of experiments used a modular pipeline called ESRC, which stands for:
- Extract – The LLM reads posts and extracts identity-relevant signals
- Search – Semantic embeddings find possible matches across large datasets
- Reason – The model evaluates and selects the most likely match
- Calibrate – Confidence scores adjust the precision–recall balance
Hacker News and LinkedIn (Scale Test)
The researchers analysed:
- 987 LinkedIn profiles
- linked to 995 Hacker News accounts
- across a pool of roughly 89,000 active Hacker News users
At 90% precision, the best AI pipeline achieved about 55% recall.
For comparison, traditional deanonymisation methods adapted from the Netflix Prize attack achieved only 0.1% recall at the same precision level.
Reddit Movie Communities
Another test looked at 9,781 Reddit users active across r/movies and five related subreddits.
Their activity was split across datasets to simulate pseudonymous cross-platform behaviour.
At 90% precision:
- High-effort reasoning achieved 8.5% recall
- Low-effort reasoning achieved 5.2% recall
At 99% precision, recall improved from 1.4% to 2.8% when more reasoning was applied.
Traditional baselines operating on the same data produced near-zero recall.
Across all experiments, one pattern was clear:
LLM-based extraction, search, and reasoning dramatically outperform older deanonymisation techniques.
Why Social Networks May Struggle to Stop This
You might assume AI companies could simply add guardrails to prevent these attacks.
But according to the researchers, it is not that simple.
The attack pipeline consists of tasks that are individually harmless:
- summarising a profile
- searching for information
- ranking potential matches
None of these steps triggers standard safety guardrails.
The attacks also rely on publicly available models, standard APIs, and open-source systems, many of which have no guardrails at all.
And there is another issue: the more reasoning power an AI model has, the more effective these attacks become. As models improve, the methods will likely improve as well.
For years, pseudonymous users were protected by a simple barrier: cost.
Identifying someone behind an anonymous account required skilled investigators and many hours of manual work. Doing it at scale was simply too expensive.
AI has now removed that barrier.
These processes can be automated cheaply and run across entire populations.
The Big Question
So yes, AI could help expose dangerous trolls and online abusers.
But the same tools could also be used against the very people who most depend on anonymity for safety — journalists, dissidents, and vulnerable communities.
Which brings us to the big question:
Is this an example of AI being used for good — or does it introduce an entirely new set of risks?
What do you think?
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About the Podcast Host
AI and Communications Specialist | Author | Podcaster | International Speaker
Joanne Sweeney is a recognised leader in digital marketing and digital transformation, specialising in the practical application of AI to enhance communication strategies. The founder of Digital Training Institute, Joanne, empowers leaders and communications professionals to leverage AI responsibly and effectively, ensuring transparency, trust, and tangible results.
She completed the Harvard Kennedy School Executive Programme in AI and Public Policy this summer before launching her fourth book AI Playbook, in September. Joanne has also published two editions of Public Sector Marketing Pro, which were preceded by her first title, Social Media Under Investigation Law Enforcement and the Social Web.
Her books have become go-to resources for governments, public sector and policing agencies, as well as marketing and communications professionals, all navigating the challenges of the changing Internet.
Joanne is the host of the AI SIX Podcast, a daily six-minute podcast that provides actionable insights, expert interviews, and practical advice to help professionals navigate the new world of work.
