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Since I run all my mail through Mailwasher before it ever hits my machine, I routinely blacklist/bounce/delete anything of this nature. But WHAT ARE THEY? If I open one, will it pop a truly objectionable porn photo onto my desktop? or will it infect my network with something I have to do a complete wipe to remove? or will it send me to Mars in the next century? [THAT would be fun!....]
Anyone have a definitive answer?
OK, I maybe have some Idea. Anyone can tell a spam by it subject line right? Random words in it gets them trough most filters and when they do, they tease the reader more than 'vanilla' SPAM.
Try something, save such mails and open them with some text editor. I bet you can see frames or iframes in the source code of some HTML mail. I believe most email clients are set not to display such content for security reasons. They can be used to 'force' windows to load some nasties like scumware, virus or phone dialers. Since most e-mail clients wont load them you see random words. I am not sure what those are about in body. Can they be used as a key or tracking device?
I have no idea... maybe some 'security consultants' around here can tell.
I think they _are_ a tracking device.
Say you send 1 million of random-words spam emails.
The prog you use for sending that, keeps track of the semantic areas of words in [the subject of] every email sent.
And, because [I suppose..] each of these emails has at least an external image inside ['img src=http://etcetc/uniqueid'], then you're able to summarize which semantic areas are more 'fruitful', in terms of people who open these emails.
Ugh. *sigh* Okay, I'll just keep doin' what I'm doin' then! If something nasty occurs it'll be server-side instead of "at home". I think I may email my ISP though, just to let them know what's been observed. I'm assuming they already know, but then one never knows does one?
Thanks all for the replies. Sorry I can't help lawman and Macguru with the "objectionable porn" situation - I'm NOT going there!
Check out Paul Graham's articles about Bayesian filtering to learn more: [paulgraham.com...]