That is the way some email clients, say GroupWise does it. There is an option to put all mail from new receivers in the junk folder for perusal. OTOH should simple graylisting do the same thing. But yes, an option to deduct points from previously unseen senders would be useful.
-- Med vänlig hälsning Anders Gustafsson, ingenjör anders.gustafs...@pedago.fi | Support +358 18 12060 | Direkt +358 9 315 45 121 | Mobil +358 40506 7099 Pedago interaktiv ab, Nygatan 7 B , AX-22100 MARIEHAMN, ÅLAND, FINLAND >>> Mark London <m...@psfc.mit.edu> 2024-06-26 00:38 >>> Bill - Thanks for the response. As an aside, it would be nice (though impossible?) for a spam filter to be more suspicious of emails coming from a new email address, that is not in my Sent folder or my Inbox. FWIW. - Mark On 6/25/2024 11:21 AM, Bill Cole wrote: > Mark London <m...@psfc.mit.edu> > is rumored to have said: > >> I received a spam email with the text below, that wasn't caught by >> Spamassasin (at least mine). The text actually looks like something >> that was generated using ChatGPT. In any event, I put the text >> through ChatGPT, and asked if it looked like spam. At the bottom of >> this email , is it's analysis. I've not been fully reading this >> group. Has there been any work to allow Spamassassin to use AI? > > "Artificial intelligence" does not exist. It is a misnomer. > > Large language models like ChatGPT have a provenance problem. There's > no way to know why exactly the model "says" anything. In a single > paragraph, ChatGPT is capable of making completely and directly > inconsistent assertions. The only way to explain that is that despite > appearances, a request to answer the ham/spasm question generates text > with no semantic connection to the original, but which seems like an > explanation. > > SpamAssassin's code and rules all come from ASF committers, and the > scores are determined by examining the scan results from contributors > and optimizing them to a threshold of 5.0. Every scan of a message > results in a list of hits against documented rules. The results can be > analyzed and understood. > > We know that ChatGPT and other LLMs that are publicly available have > been trained on data to which they had no license. There is no way to > remove any particular ingested data. There's no way to know where any > particular LLM will have problems and no way to fix those problems. > This all puts them outside of the boundaries we have as an ASF > project. However, we do have a plugin architecture, so it is possible > for 3rd parties to create a plugin for LLM integration. > >
