In a third experiment, the researchers took 5,000 users from the Netflix dataset and added another 5,000 “distraction” identities of people not in the results. They then added to the list of 10,000 candidate profiles 5,000 query distractors comprising users who appear only in a query set, with no true match in the candidate pool.

Compared to a classical baseline that mimics the Netflix Prize attack to LLM deanonymization, the latter far outperformed the former.

The researchers wrote:

(a) The precision of classical attacks drops very fast, explaining its low recall. In contrast, the precision of LLM-based attacks decays more gracefully as the attacker makes more guesses. (b) The classical attack almost fails completely even at moderately low precision. In contrast, even the simplest LLM attack (Search) achieves non-trivial recall at low precision, and extending it with Reason and Calibrate steps doubles Recall @99% Precision.

The results show that LLMs, while still prone to false positives and other weaknesses, are quickly outstripping more traditional, resource-intensive methods for identifying users online.

The researchers went on to propose mitigations, including platforms enforcing rate limits on API access to user data, detecting automated scraping, and restricting bulk data exports. LLM providers could also monitor for the misuse of their models in deanonymization attacks and build guardrails that make models refuse deanonymization requests.

Of course, another option is for people to dramatically curb their use of social media, or at a minimum, regularly delete posts after a set time threshold.

If LLMs’ success in deanonymizing people improves, the researchers warn, governments could use the techniques to unmask online critics, corporations can assemble customer profiles for “hyper-targeted advertising,” and attackers could build profiles of targets at scale to launch highly personalized social engineering scams.

“Recent advances in LLM capabilities have made it clear that there is an urgent need to rethink various aspects of computer security in the wake of LLM-driven offensive cyber capabilities, the researchers warned. “Our work shows that the same is likely true for privacy as well.”