X's Open Source Decision Raises Questions About Anonymous Account Security

Algorithm Transparency Ignites User Privacy Debates

X platform (formerly Twitter) has released its entire recommendation algorithm as open source, following a decision announced by CEO Elon Musk. This move is stated to aim at increasing transparency regarding the platform's operations and responding to European Union regulations. However, a deep examination of the shared code has raised new and serious concerns about user privacy.

'User Action Sequence' Creates a Behavioral Fingerprint

Examination of the code repository published by the platform by security researchers and open-source intelligence (OSINT) enthusiasts revealed the existence of a complex system called the 'User Action Sequence'. This system records every user action on the platform. Thousands of data points are collected, such as milliseconds spent scrolling a post, reactions to specific content types, which accounts are blocked, and interaction timings.

While this collected data is used to suggest more relevant content to the user and extend their time on the platform, it also creates an extremely detailed 'behavioral fingerprint'. Researchers emphasize that this digital trace reflects users' habits and is very difficult to alter.

Risk of Identifying Anonymous Accounts Increases

OSINT expert @Harrris0n, who shared analyses on the subject, suggested that by using tools like 'Candidate Isolation' in the open-source code, the behavioral fingerprint of a known account could be compared with anonymous accounts. It is stated that 'abnormally high' matches could be found in similarity analyses.

The expert notes that, theoretically, thanks to the action sequence encoder and similarity search tools shared in the code repository, even a user's accounts on different platforms could be linked. This situation calls into question the effectiveness of traditional anonymous (burner) account usage. This development also reignites debates on artificial intelligence and data ethics. Similarly, the misuse of deepfake technologies demonstrates how diverse the threats to individuals' digital identities have become.

Low Barrier and Potential Consequences

Allegedly, the technical barrier to creating such a detection tool is quite low. The public availability of the core code means malicious actors can also access this information. It is noted that the only missing piece is verified anonymous account data, but some groups tracking threat actors might already possess this data.

Experts highlight that the data collected by social media platforms to improve user experience can create unforeseen security and privacy risks. X's move has reopened the global debate on the delicate balance between algorithmic transparency and individual privacy rights.