Live › vulnerability › CVE-2026-121564
whatstk is a Python library for parsing WhatsApp chat exports. Its merge step silently discards messages from one export whenever their dates fall inside the date range of the other, so the merged chat omits content with no warning.
Summary written from the sources below. Check the originals before acting.
First seen by InfraStack 2026-11-03 16:21 UTC. All times on this page are UTC.
Teams that use whatstk to merge two or more WhatsApp chat exports with overlapping date ranges, for example in forensic review or when building training data. Code that parses a single export, or never calls merge, is not affected. The flaw only loses data during merging, so it does not give an attacker code execution.
No product ranges from the sources yet.
pip show whatstk; affected versions are from 0.3.0 before 0.9.0.BaseChat.merge() or merge_chats() in your code that combine overlapping chat exports.0.9.0 or later.Each row is the first time that source carried this signal. The analyst’s rows are its own findings.
Incomplete Comparison with Missing Factors (CWE-1023) in merge_chats() (whatstk/utils/chat_merge.py), reached from BaseChat.merge(), in whatstk from 0.3.0 before 0.9.0 allows a party that supplies the chat exports to cause messages to be absent from the merged output with no indication that anything was removed, because the merge keeps whole the chat whose first message is oldest and appends from the other only the messages that are strictly newer than the last message of the first. Every message of the second chat that falls inside the date range already covered by the first is therefore discarded. The two chats are compared on their date ranges and not on the identity of the individual messages, so a message held by only one of the two exports is dropped whenever its date falls inside the range of the other, and both branches of _merge_two_chats() behave this way. The merged result is returned without a warning and without a count of what was removed. This results in a merged chat that omits messages the inputs contain, in a library whose documented uses include forensic review and the generation of training data.
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